Talend Components - 7.3

tAccessBulkExec	Offers gains in performance when carrying out Insert operations in an Access database.
tAccessClose	Closes an active connection to the Access database so as to release occupied resources.
tAccessCommit	Commits in one go a global transaction instead of doing that on every row or every batch, and provides gain in performance, using a unique connection.
tAccessConnection	Opens a connection to the specified database that can then be reused in the subsequent subJob or subJobs.
tAccessInput	Reads a database and extracts fields based on a query.
tAccessOutput	Writes, updates, makes changes or suppresses entries in a database.
tAccessOutputBulk	Prepares the file which contains the data used to feed the Access database.
tAccessOutputBulkExec	Executes an Insert action on the data provided, in an Access database.
tAccessRollback	Cancels the transaction commit in the connected database and avoids to commit part of a transaction involuntarily.
tAccessRow	Executes the SQL query stated onto the specified database.

• Inserting data in parent/child tables

tAmazonAuroraInvalidRows	Checks Amazon Aurora database rows against specific Data Quality patterns (regular expression) or Data Quality rules (business rule). Only MySQL is supported.
tAmazonAuroraValidRows	Checks Amazon Aurora database rows against specific Data Quality patterns (regular expression) or Data Quality rules (business rule). Only MySQL is supported.
tAmazonAuroraClose	Closes an active connection to an Amazon Aurora database instance to release the occupied resources.
tAmazonAuroraCommit	Commits in one go a global transaction instead of doing that on every row or every batch, and provides gain in performance, using a unique connection.
tAmazonAuroraConnection	Opens a connection to an Amazon Aurora database instance that can then be reused by other Amazon Aurora components.
tAmazonAuroraInput	Reads an Amazon Aurora database and extracts fields based on a query.
tAmazonAuroraOutput	Writes, updates, makes changes or suppresses entries in an Amazon Aurora database.
tAmazonAuroraRollback	Rolls back any changes made in the Amazon Aurora database to prevent partial transaction commit if an error occurs.
tAmazonAuroraRow	Executes query statements on a specified Amazon Aurora database table.

• Handling data with Amazon Aurora

tDynamoDBConfiguration	Stores connection information and credentials to be reused by other DynamoDB components.
tDynamoDBLookupInput	Executes a database query with a strictly defined order which must correspond to the schema definition.
tDynamoDBInput	Retrieves data from an Amazon DynamoDB table and sends them to the component that follows for transformation.
tDynamoDBOutput	Creates, updates or deletes data in an Amazon DynamoDB table.

• Writing and extracting JSON documents from DynamoDB

tAmazonEMRListInstances	Lists the details about the instance groups in a cluster on Amazon EMR (Elastic MapReduce).
tAmazonEMRManage	Launches or terminates a cluster on Amazon EMR (Elastic MapReduce).
tAmazonEMRResize	Adds or resizes a task instance group in a cluster on Amazon EMR (Elastic MapReduce).

• Managing an Amazon EMR cluster

• Writing server-side KMS encrypted data on EMR

tAmazonMysqlClose	Closes the transaction committed in the connected DB.
tAmazonMysqlCommit	Commits in one go a global transaction instead of doing that on every row or every batch, and provides gain in performance, using a unique connection.
tAmazonMysqlConnection	Opens a connection to the specified database that can then be reused in the subsequent subJob or subJobs.
tAmazonMysqlInput	Reads a database and extracts fields based on a query.
tAmazonMysqlOutput	Writes, updates, makes changes or suppresses entries in a database.
tAmazonMysqlRollback	Cancels the transaction commit in the connected database and avoids to commit part of a transaction involuntarily.
tAmazonMysqlRow	Executes the SQL query stated onto the specified database.

tAmazonOracleClose	Closes the transaction committed in the connected database.
tAmazonOracleCommit	Commits in one go a global transaction instead of doing that on every row or every batch, and provides gain in performance, using a unique connection.
tAmazonOracleConnection	Opens a connection to the specified database that can then be reused in the subsequent subJob or subJobs.
tAmazonOracleInput	Reads a database and extracts fields based on a query.
tAmazonOracleOutput	Writes, updates, makes changes or suppresses entries in a database.
tAmazonOracleRollback	Cancels the transaction commit in the connected database and avoids to commit part of a transaction involuntarily.
tAmazonOracleRow	Executes the SQL query stated onto the specified database.

tRedshiftConfiguration	Reuses the connection configuration to a Redshift database in the same Job.
tRedshiftLookupInput	Reads a Redshift database and extracts fields based on a query.
tAmazonRedshiftManage	Manages Amazon Redshift clusters and snapshots.
tRedshiftBulkExec	Loads data into Amazon Redshift from Amazon S3, Amazon EMR cluster, Amazon DynamoDB, or remote hosts.
tRedshiftClose	Closes the transaction committed in the connected DB.
tRedshiftCommit	Provides gain in performance.
tRedshiftConnection	Opens a connection to the specified database that can then be reused in the subsequent subJob or subJobs.
tRedshiftInput	Reads data from a database and extracts fields based on a query so that you may apply changes to the extracted data.
tRedshiftOutput	Writes, updates, modifies or deletes the data in a database.
tRedshiftOutputBulk	Prepares a delimited/CSV file that can be used by tRedshiftBulkExec to feed Amazon Redshift.
tRedshiftOutputBulkExec	Executes the Insert action on the data provided.
tRedshiftRollback	Cancels the transaction commit in the Redshift database to avoid committing part of a transaction involuntarily.
tRedshiftRow	Acts on the actual DB structure or on the data (although without handling data), depending on the nature of the query and the database.
tRedshiftUnload	Unloads data on Amazon Redshift to files on Amazon S3.

• Handling data with Redshift
• Loading/unloading data to/from Amazon S3

tS3Configuration	Reuses the connection configuration to S3 in the same Job. The Spark cluster to be used reads this configuration to eventually connect to S3.
tS3Input	Reads data from a given S3N system (S3 Native Filesystem).
tS3Output	Writes data into a given S3 filesystem.
tS3BucketCreate	Creates a bucket on Amazon S3.
tS3BucketDelete	Deletes an empty bucket from Amazon S3.
tS3BucketExist	Verifies if the specified bucket exists on Amazon S3.
tS3BucketList	Lists all the buckets on Amazon S3.
tS3Close	Shuts down a connection to Amazon S3, thus releasing the network resources.
tS3Connection	Establishes a connection to Amazon S3 to store and retrieve data.
tS3Copy	Copies an Amazon S3 object from a source bucket to a destination bucket.
tS3Delete	Deletes a file from Amazon S3.
tS3Get	Retrieves a file from Amazon S3.
tS3List	Lists the files on Amazon S3 based on the bucket/file prefix settings.
tS3Put	Uploads data onto Amazon S3 from a local file or from cache memory via the streaming mode.

• Writing and reading data from S3 (Databricks on AWS)
• Writing server-side KMS encrypted data on EMR
• Copying an S3 object from one bucket to another
• Exchange files with Amazon S3
• Listing files with the same prefix from a bucket
• Retrieving data from an S3 object in Studio
• Tagging S3 objects
• Verifing the absence of a bucket, creating it and listing all the S3 buckets

tSQSConnection	Opens a connection to Amazon Simple Queue Service that can then be reused by other SQS components.
tSQSInput	Retrieves one or more messages, with a maximum limit of ten messages, from an Amazon SQS (Simple Queue Service) queue.
tSQSMessageChangeVisibility	Changes the visibility timeout of a specified message in an Amazon SQS (Simple Queue Service) queue.
tSQSMessageDelete	Deletes a specified message from an Amazon SQS (Simple Queue Service) queue.
tSQSOutput	Delivers one or more messages to an Amazon SQS (Simple Queue Service) queue.
tSQSQueueAttributes	Gets attributes for a specified Amazon SQS (Simple Queue Service) queue.
tSQSQueueCreate	Creates a new Amazon SQS (Simple Queue Service) queue.
tSQSQueueDelete	Deletes an Amazon SQS (Simple Queue Service) queue.
tSQSQueueList	Iterates and lists the URL of Amazon SQS (Simple Queue Service) queues in a specified region.
tSQSQueuePurge	Purges messages in an Amazon SQS (Simple Queue Service) queue.

• Delivering messages to an Amazon SQS queue
• Listing Amazon SQS queues in an AWS region
• Retrieving messages from an Amazon SQS queue

tApacheLogInput	Reads the access-log file for an Apache HTTP server.

• Reading an Apache access-log file

tFileArchive	Creates a new zip, gzip, or tar.gz archive file from one or more files or folders.
tFileUnarchive	Decompresses an archive file for further processing, in one of the following formats: *.tar.gz , *.tgz, *.tar, *.gz and *.zip.

• Comparing unzipped files
• Zipping files using a tFileArchive

tFileInputARFF	Reads an ARFF file row by row to split them up into fields and then sends the fields as defined in the schema to the next component.
tFileOutputARFF	Writes an ARFF file that holds data organized according to the defined schema.

• Displaying the content of a ARFF file

tAS400Close	Closes the transaction committed in the connected database.
tAS400Commit	Commits in one go a global transaction instead of doing that on every row or every batch, and provides gain in performance, using a unique connection.
tAS400Connection	Opens a connection to the specified database that can then be reused in the subsequent subJob or subJobs.
tAS400Input	Reads a database and extracts fields based on a query.
tAS400LastInsertId	Obtains the primary key value of the record that was last inserted in an AS/400 table.
tAS400Output	Writes, updates, makes changes or suppresses entries in a database.
tAS400Rollback	Cancels the transaction commit in the connected database and avoids to commit part of a transaction involuntarily.
tAS400Row	Executes the SQL query stated onto the specified database.

• Handling data with AS/400

tAvroInput	Extracts records from any given Avro format files for other components to process the records.
tAvroOutput	Receives data flows from the processing component placed ahead of it and writes the data into Avro format files in a given distributed file system.
tAvroStreamInput	Listens on a given directory, reads data from Avro files once they are created and sends this data to the component that follows.

• Filtering Avro format employee data

tAzureAdlsGen2Input	Retrieves data from an ADLS Gen2 file system of an Azure storage account and passes the data to the subsequent component connected to it through a Main>Row link.
tAzureAdlsGen2Output	Uploads incoming data to an ADLS Gen2 file system of an Azure storage account in the specified format.
tAzureFSConfiguration	Provides authentication information for Spark to connect to a given Azure file system.

• Accessing Azure ADLS Gen2 storage
• Writing and reading data from Azure Data Lake Storage using Spark (Azure Databricks)

tAzureFSConfiguration	Provides authentication information for Spark to connect to a given Azure file system.
tAzureStorageConnection	Uses authentication and the protocol information to create a connection to the Microsoft Azure Storage system that can then be reused by other Azure Storage components.
tAzureStorageContainerCreate	Creates a new storage container used to hold Azure blobs (Binary Large Object) for a given Azure storage account.
tAzureStorageContainerDelete	Automates the removal of a given blob container from the space of a specific storage account.
tAzureStorageContainerExist	Automates the verification of whether a given blob container exists or not within a storage account.
tAzureStorageContainerList	Lists all containers in a given Azure storage account.
tAzureStorageDelete	Deletes blobs from a given container for an Azure storage account according to the specified blob filters.
tAzureStorageGet	Retrieves blobs from a given container for an Azure storage account according to the specified filters applied on the virtual hierarchy of the blobs and then write selected blobs in a local folder.
tAzureStorageList	Lists blobs in a given container according to the specified blob filters.
tAzureStoragePut	Uploads local files into a given container for an Azure storage account.

• Creating a container in Azure Storage
• Retrieving files from a Azure Storage container

tAzureStorageConnection	Uses authentication and the protocol information to create a connection to the Microsoft Azure Storage system that can then be reused by other Azure Storage components.
tAzureStorageQueueCreate	Creates a new queue under a given Azure storage account.
tAzureStorageQueueDelete	Deletes a specified queue permanently under a given Azure storage account.
tAzureStorageQueueInput	Retrieves one or more messages from the front of an Azure queue.
tAzureStorageQueueInputLoop	Runs an endless loop to retrieve messages from the front of an Azure queue.
tAzureStorageQueueList	Returns all queues associated with the given Azure storage account.
tAzureStorageQueueOutput	Adds messages to the back of an Azure queue.
tAzureStorageQueuePurge	Purges messages in an Azure queue.

tAzureStorageConnection	Uses authentication and the protocol information to create a connection to the Microsoft Azure Storage system that can then be reused by other Azure Storage components.
tAzureStorageInputTable	Retrieves a set of entities that satisfy the specified filter criteria from an Azure storage table.
tAzureStorageOutputTable	Performs the defined action on a given Azure storage table and inserts, replaces, merges or deletes entities in the table based on the incoming data from the preceding component.

• Handling data with Microsoft Azure Table storage

tAzureSynapseBulkExec	Loads data into an Azure Synapse Analytics table from either Azure Blob Storage or Azure Data Lake Storage.
tAzureSynapseClose	Closes an active connection to an Azure Synapse Analytics database.
tAzureSynapseCommit	Commits in one go a global transaction instead of doing that on every row or every batch and thus provides gain in performance.
tAzureSynapseConnection	Opens a connection to an Azure Synapse Analytics database.
tAzureSynapseInput	Reads data and extracts fields based on a query from an Azure SQL Data Warehouse database.
tAzureSynapseOutput	Writes, updates, makes changes or suppresses entries in an Azure SQL Data Warehouse database.
tAzureSynapseRollback	Cancels the transaction commit in the connected Azure SQL Data Warehouse database to prevent partial transaction commit if an error occurs.
tAzureSynapseRow	Executes an SQL query stated on an Azure Synapse Analytics database.

tBonitaDeploy	Deploys a specific Bonita process to a Bonita Runtime.
tBonitaInstantiateProcess	Starts an instance for a specific process deployed in a Bonita Runtime engine.

• Executing a Bonita process via a Talend Job
• Outputting the process instance UUID over the Row > Main link

tBoxConnection	Creates a Box connection that the other Box components can reuse.
tBoxCopy	Copies or moves a given folder or file from Box.
tBoxDelete	Removes a given folder or file from Box.
tBoxGet	Downloads a selected file from a Box account.
tBoxList	Lists the files stored in a specified directory in Box.
tBoxPut	Uploads files to a Box account.

• Uploading and downloading files from Box

tBufferInput	Retrieves data bufferized via a tBufferOutput component, for example, to process it in another subJob.
tBufferOutput	Collects data in a buffer in order to access it later via webservice for example.

• Buffering data to be used as a source system
• Buffering data
• Retrieving bufferized data
• Returning a value from a child Job to the parent Job

tBRMS	Applies Drools business rules to an incoming flow and writes the output data to an XML file.
tRules	Uses business rules defined in a Drools file of .xls or .drl format in order to filter data.

• Applying business rules to an Excel file to filter data
• Extracting client data according to business rules stored in an external file
• Extracting zip codes using DRL rules you create from the Studio

tCassandraConfiguration	Enables the reuse of the connection configuration to a Cassandra server in the same Job.
tCassandraLookupInput	Extracts the desired data from a standard or super column family of a Cassandra keyspace so as to apply changes to the data.
tCassandraBulkExec	Improves performance during Insert operations to a Cassandra column family.
tCassandraClose	Disconnects a connection to a Cassandra server so as to release occupied resources.
tCassandraConnection	Enables the reuse of the connection it creates to a Cassandra server.
tCassandraInput	Extracts the desired data from a standard or super column family of a Cassandra keyspace so as to apply changes to the data.
tCassandraOutput	Writes data into or deletes data from a column family of a Cassandra keyspace.
tCassandraOutputBulk	Prepares an SSTable of large size and processes it according to your needs before loading this SSTable into a column family of a Cassandra keyspace.
tCassandraOutputBulkExec	Improves performance during Insert operations to a column family of a Cassandra keyspace.
tCassandraRow	Acts on the actual DB structure or on the data, depending on the nature of the query and the database.

• Handling data with Cassandra

tAS400CDC	Addresses data extraction and transportation needs.
tDB2CDC	Extracts the changes done to the source operational data and makes them available to the target system(s) using database CDC views.
tInformixCDC	Extracts the data from a source system which has changed since the last extraction and transports it to another/other system(s).
tIngresCDC (deprecated)	Extracts source system data that has changed since the last extraction and transports it to another/other system(s).
tMSSqlCDC	Extracts the changes made to the source operational data and makes them available to the target system(s) using database CDC views.
tMysqlCDC	Extracts only the changes made to the source operational data and makes them available to the target system(s) using database CDC views.
tOracleCDC	Extracts source system data that has changed since the last extraction and transports it to another/other system(s).
tOracleCDCOutput	Synchronizes data changes in the Oracle XStream CDC mode.
tPostgresqlCDC	Addresses data extraction and transportation needs, only extracts the changes made to the source operational data and makes them available to the target system(s) using database CDC views.
tSybaseCDC	Extracts source system data that has changed since the last extraction and transports it to another/other system(s).
tTeradataCDC	Extracts source system data that has changed since the last extraction and transports it to another system(s) using the CDC Trigger mode.

• Extracting and synchronizing data changes using XStream mode
• Populating a MySQL data warehouse
• Populating an Oracle data warehouse
• Retrieving modified data using CDC
• Retrieving modified data using Oracle CDC Redo log mode

tBarChart	Generates a bar chart from the input data to ease technical analysis.
tLineChart	Reads data from an input flow and transforms the data into a line chart in a PNG image file to ease technical analysis.

• Creating a bar chart from the input data
• Creating a line chart to ease trend analysis

tCloudStart	Starts instances on Amazon EC2 (Amazon Elastic Compute Cloud).
tCloudStop	Changes the status of a launched instance on Amazon EC2 (Amazon Elastic Compute Cloud).

tCombinedSQLAggregate	Provides a set of matrix based on values or calculations.
tCombinedSQLFilter	Filters data by reorganizing, deleting or adding columns based on the source table and to filter the given data source using the filter conditions.
tCombinedSQLInput	Extracts fields from a database table based on its schema definition.
tCombinedSQLOutput	Inserts records from the incoming flow to an existing database table.

• Filtering and aggregating table columns directly on the DBMS

tContextDump	Copies the context setup of the current Job to a flat file, a database table, etc., which can then be used by tContextLoad.
tContextLoad	Loads a context from a flow.

• Reading data from different MySQL databases using dynamically loaded connection parameters

tCosmosDBSQLAPIInput	Retrieves data from a Cosmos database collection through SQL API.
tCosmosDBSQLAPIOutput	Inserts, updates, upserts or deletes documents in a Cosmos database collection based on the incoming flow from the preceding component through SQL API.
tCosmosDBBulkLoad	Imports data files in different formats (CSV, TSV or JSON) into the specified Cosmos database so that the data can be further processed.
tCosmosDBConnection	Creates a connection to a CosmosDB database and reuse that connection in other components.
tCosmosDBInput	Retrieves certain documents from a Cosmos database collection by supplying a query document containing the fields the desired documents should match.
tCosmosDBOutput	Inserts, updates, upserts or deletes documents in a Cosmos database collection based on the incoming flow from the preceding component in the Job.
tCosmosDBRow	Executes the commands of the Cosmos database.

tCouchbaseDCPInput	Queries the documents from the Couchbase database, under the Database Change Protocol (DCP), a streaming protocol.
tCouchbaseDCPOutput	Upserts documents in the Couchbase database based on the incoming flat data from preceding components, under the Database Change Protocol (DCP), a streaming protocol.
tCouchbaseInput	Queries the documents from the Couchbase database.
tCouchbaseOutput	Upserts documents in the Couchbase database based on the incoming flat data from preceding components.

• Querying JSON documents from a Couchbase database with a N1QL query

tCyberarkInput	Retrieves the content of an secret object (usually, a password) stored in a Cyberark vault at runtime. The retrieved content is stored in the after variable SECRET, which can be referenced by any subsequent components in the Job. The content can also be passed to the subsequent component in a column named secret through a Row > Main connection.

• Accessing a password-protected file

tHConvertFile	Uses Talend Data Mapper structures to perform a conversion from one representation to another, as a Spark Batch execution.
tHMap	Executes transformations (called maps) between different sources and destinations by harnessing the capabilities of Talend Data Mapper , available in the Mapping perspective.
tHMapFile	Runs a Talend Data Mapper map where input and output structures may differ, as a Spark batch execution.
tHMapInput	Runs a Talend Data Mapper map where input and output structures may differ, as a Spark batch execution, and sends the data for use by a downstream component.
tHMapRecord	Runs a Talend Data Mapper map where input and output structures may differ, as a Spark streaming execution.

• Connecting tHMapRecord to multiple outputs
• Generating the Output Using tHMap with Multiple Schema Inputs
• Generating the Output using tHMap with Multiple Payload Inputs
• Handling errors
• Transforming data in a Spark environment
• Transforming from a Data Integration schema to a complex content schema
• Using Talend Data Integration metadata
• Using Talend Data Mapper metadata

tDataprepRun	Applies a preparation made using Talend Data Preparation in a standard Data Integration Job.
tDatasetInput	Creates a flow with data from a Talend Data Preparation dataset.
tDatasetOutput	Creates a dataset in Talend Data Preparation.

• Applying a preparation to a data sample in an Apache Spark Batch Job
• Applying a preparation to a data sample in an Apache Spark Streaming Job
• Creating a dataset from a Job
• Dynamically selecting a preparation at runtime according to the input
• Preparing data from a database in a Talend Job
• Promoting a Job leveraging a preparation across environments

tAddressRowCloud	Verifies and formats international addresses in the Cloud by using online services.
tBatchAddressRowCloud	Uses batch processing to parse address data and get formatted addresses quickly, accurately and without installing any software.

• Editing the mapping of the verification codes from address validation providers to Talend verification levels
• Parsing addresses against reference data in the Cloud
• Parsing addresses against reference data in the Cloud using batch processing

tMatchIndex	Indexes a clean and deduplicated data set in ElasticSearch for continuous matching purposes.
tMatchIndexPredict	Compares a new data set with a lookup data set stored in ElasticSearch, using tMatchIndex. tMatchIndexPredict outputs unique records and suspect duplicates in separate files.

• Doing continuous matching using tMatchIndexPredict
• Indexing a reference data set in Elasticsearch

tExtractRegexFields	Extracts data and generates multiple columns from a formatted string using regex matching.
tPatternExtract	Outputs all data that match a given pattern. You can then implement any required operation on the extracted data.

• Extracting name, domain and TLD from e-mail addresses
• Extracting only the data that corresponds to a defined pattern from a delimited file

tMatchGroup	Creates groups of similar data records in any source data including large volumes of data by using one or several match rules.
tRecordMatching	Ensures the data quality of any source data against a reference data source.

• Grouping output data in separate flows according to the minimal distance computed in each record
• Matching customer data through multiple passes
• Matching data through multiple passes using Map/Reduce components
• Matching entries using the Q-grams and Levenshtein algorithms
• Using a custom matching algorithm to match entries
• Using survivorship functions to merge two records and create a master record

tDataDecrypt	Decrypts data encrypted with the tDataEncrypt component.
tDataEncrypt	Protects data by transforming it into unreadable cipher text.
tDataMasking	Hides original data with random characters or figures to protect the actual data while having a functional substitute for occasions when it is not advisable to show sensitive real data.
tDataShuffling	Shuffles the data from in an input table to protect the actual data while having a functional data set. Data will remain usable for purposes such as testing and training.
tDataUnmasking	Unmasks data masked with the tDataMasking component to retrieve the original data.
tDuplicateRow	Creates duplicates with meaningful data for data quality functional testing purposes.
tPatternMasking	Masks data that follows a specific pattern and can transform the original data in consistent manner, if needed.
tPatternUnmasking	Unmasks data masked with the tPatternMasking component to retrieve the original data.

• Altering data values to restrict the use of actual sensitive data
• Encrypting and decrypting back sensitive data
• Generating duplicate data from an input flow
• Masking Australian phone numbers
• Masking Medicare beneficiary identifiers
• Shuffling data values to restrict the use of actual sensitive data
• Unmasking Australian phone numbers

tRuleSurvivorship	Creates the single representation of an entity according to business rules and can create a master copy of data for Master Data Management.
tSurviveFields	Centralizes data from various and heterogeneous sources to create a master copy of data for MDM.
tUniqRow	Ensures data quality of input or output flow in a Job.

• Converting the Standard Job to a Spark Batch Job
• Creating a clean data set from the suspect pairs labeled by tMatchPredict and the unique rows computed by tMatchPairing
• Deduplicating entries based on dynamic schema
• Deduplicating entries using Map/Reduce components
• Merging the content of several rows using different columns as rank values
• Modifying the rule file manually to code the conditions you want to use to create a survivor
• Selecting the best-of-breed data from a group of duplicates to create a survivor
• Deduplicating entries

tVerifyEmail	Verifies if email addresses comply with specific rules and corrects addresses that do not match the rules by using the content from specific columns.

• Verifying email addresses against column content and domain names

tChangeFileEncoding	Transforms the character encoding of a given file and generates a new file with the transformed character encoding.

• Transforming the character encoding of a file

tBlockedFuzzyJoin	Helps ensuring the data quality of any source data against a reference data source.
tFuzzyJoin	Joins two tables by doing a fuzzy match on several columns, comparing columns from the main flow with reference columns from the lookup flow and outputting the main flow data and the rejected data.
tFuzzyMatch	Compares a column from the main flow with a reference column from the lookup flow and outputs the main flow data displaying the distance.
tFuzzyUniqRow	Compares columns in the input flow by using a defined matching method and collects the encountered duplicates.

• Checking the Levenshtein distance of 0 in first names
• Checking the Levenshtein distance of 1 or 2 in first names
• Checking the Metaphonic distance in first name
• Comparing four columns using different matching methods and collecting encountered duplicates
• Doing a fuzzy match on two columns and outputting the main and rejected data
• Doing a fuzzy match on two columns and outputting the match, possible match and non match values

tGoogleAddressRow	Converts human-readable addresses into geographic coordinates and other geographic information.
tGoogleGeocoder	Converts human-readable addresses into geographic coordinates.
tGoogleMapLookup	Obtains detailed geographic information using geographic coordinates and address information.

• Obtaining detailed geographic information using address and geographic coordinates
• Obtaining detailed geographic information using address information
• Obtaining geographic coordinates using address information

tGenKey	Generates a functional key from the input columns, by applying different types of algorithms on each column and grouping the computed results in one key, then outputs this key with the input columns.
tAddCRCRow	Provides a unique ID which helps improving the quality of processed data. CRC stands for Cyclical Redundancy Checking.

• Comparing columns and grouping in the output flow duplicate records that have the same functional key
• Generating functional keys in the output flow
• Adding a surrogate key to a file

tLoqateAddressRow	Parses, verifies, cleanses, standardizes, transliterates, and formats international addresses.

• Parsing addresses against Loqate data

tMatchModel	Generates the matching model that is used by the tMatchPredict component to automatically predict the labels for the suspect pairs and groups records which match the label(s) set in the component properties.
tMatchPairing	Enables you to compute pairs of suspect duplicates from any source data including large volumes in the context of machine learning on Spark.
tMatchPredict	Labels suspect records automatically and groups suspect records which match the label(s) set in the component properties.

• Computing suspect pairs and suspect sample from source data
• Computing suspect pairs and writing a sample in Talend Data Stewardship
• Generating a matching model
• Generating a matching model from a Grouping campaign
• Labeling suspect pairs with assigned labels

tMelissaDataAddress	Verifies if an address is properly formatted and corrects any formatting or spelling errors in each row.
tPersonator	Ensures the quality of a US and Canadian contact database by checking, verifying, moving and appending contact data.

• Editing addresses against a Melissa Data data file
• Scenario: Verifying and enriching a database

tMSSqlInvalidRows	Extracts DB rows that match a given data quality business rule. You can then implement any required correction.
tMSSqlValidRows	Extracts DB rows that match a given data quality business rule.

tMySQLInvalidRows	Checks MySQL database rows against specific Data Quality patterns (regular expression) or Data Quality rules (business rule).
tMySQLValidRows	Checks MySQL database rows against Data Quality patterns (regular expression).

• Checking customer table against a given DQ rule to select customer records
• Reading email addresses from a DB table and retrieving specific data

tFirstnameMatch	Matches first names against a reference index in order to standardize data.

• Matching first names with a reference index

tOracleInvalidRows	Checks Oracle database rows against specific Data Quality patterns (regular expression) or Data Quality rules (business rule).
tOracleValidRows	Checks Oracle database rows against Data Quality patterns (regular expression).

tFindRegexlibExpressions	Returns a dataset holding information about all of the regular expressions that match the request sent to the web server.
tLastRegexlibExpressions	Returns a dataset holding information about the N most recent regular expressions added to the library and that match the query at http://regexlib.com.
tMultiPatternCheck	Checks all existing data in multiple columns against a given Java regular expression.
tPatternCheck	Gives two output flows: Matching Data and Non-Matching Data. The first collects all data that match a given pattern, and the second collects all data that do not match a given pattern. You can then implement any required corrections.

• Checking the data in multiple columns against patterns
• Connecting to a web service and returning a list of regular expressions

tStandardizePhoneNumber	Standardizes phone numbers according to given formats.

• Standardizing French phone numbers

tPostgresqlInvalidRows	Extracts DB rows that do not match a given data quality pattern, you can then implement any required correction.
tPostgresqlValidRows	Extracts DB rows that match a given data quality pattern.

tQASAddressIncomplete (deprecated)	Gives two output flows: Incomplete and Reject.
tQASAddressRow	Corrects any formatting or spelling errors and gives the verification status for each row.
tQASAddressUnknown (deprecated)	Gives one output flow: Unknown which collects all addresses that do not match to deliverable results in the QuickAddress data.
tQASAddressVerified (deprecated)	Gives three output flows: Verified, Interaction required, and Reject.
tQASBatchAddressRow	Corrects any formatting or spelling errors, adds missing data and gives the verification status for each row.

• Editing addresses against QAS files and giving the verification status
• Editing addresses and giving the verification status

tDqReportRun	Launches the analyses listed in a report and save the results in the data quality data mart.
tThresholdViolationAlert	Alerts to any threshold violations regarding the thresholds set on indicators in different quality analyses created in the Studio.

• Launching a profiling report from Talend Cloud Management Console
• Launching a profiling report from Talend Administration Center Web application

tReservoirSampling	Extracts a random sample data from a big data set.

• Extracting sample data from an input data set

tStandardizeRow	Normalizes the incoming data in a separate XML or JSON data flow to separate or standardize the rule-compliant data from the non-compliant data.
tIntervalMatch	Returns a value based on a Join relation.
tReplaceList	Cleanses all files before further processing.

• Extracting exact match by using Index rules
• Normalizing data using rules of basic types
• Standardizing addresses from unstructured data
• Using two parsing levels to extract information from unstructured data
• Identifying server locations based on their IP addresses
• Replacing state names with their two-letter codes

tSynonymOutput	Creates a Lucene index and feeds it with entries and the related synonyms it receives.
tSynonymSearch	Searches a given index for the reference entries matching the data you input.

• Creating a synonym index for city names
• Creating a synonym index for people names using tMap
• Searching a given index for matched reference entries
• Searching for matched reference entries for two input columns

tJapaneseNumberNormalize	Normalizes Japanese numbers (kansūji) to regular Arabic numbers.
tJapaneseTokenize	Splits Japanese text into tokens.
tJapaneseTransliterate	Converts textual data in Japanese to kana and Latin scripts.
tStem	Enables to standardize data in columns before matching this data.
tTransliterate	Converts strings from many languages of the world to a standard set of characters (Universal Coded Character Set, UCS).

• Converting Japanese numbers to Arabic numbers
• Converting words from different languages to standard set of characters
• Extracting the stems of English words from a specific DB column
• Generating stems for a list of English words
• Tokenizing Japanese text
• Transliterating Japanese text

tUniservBTGeneric (deprecated)	Executes a process created with the Uniserv product DQ Batch Suite.
tUniservRTConvertName (deprecated)	Analyzes the name elements in an address .
tUniservRTMailBulk (deprecated)	Creates the index pool for duplicate search.
tUniservRTMailOutput (deprecated)	Synchronizes the index pool that is used for duplicate search.
tUniservRTMailSearch (deprecated)	Searches for duplicate values based on a given input record and adds additional data to each record.
tUniservRTPost (deprecated)	Improves the addresses quality, which is extremely important for CRM and e-business as it is directly related to postage and advertising costs.

• Adding contacts to the mailRetrieval index pool
• Analyzing a person's name and assigning a salutation
• Checking and correcting the postal code, city and street
• Checking and correcting the postal code, city and street, as well as rejecting the unfeasible
• Creating an index pool
• Execution of a Job in the Data Quality Service Hub Studio

tSchemaComplianceCheck	Ensures the data quality of any source data against a reference data source.

• Validating data against schema

tDataStewardshipTaskDelete	Connects to Talend Data Stewardship and deletes the data stored in campaigns in the form of tasks.
tDataStewardshipTaskInput	Connects to Talend Data Stewardship and retrieves the data stored in campaigns in the form of tasks.
tDataStewardshipTaskOutput	Connects to Talend Data Stewardship and loads data into campaigns in the form of tasks. The tasks must have the same schema defined in the campaign.

• Assigning tasks dynamically in Talend Data Stewardship
• Deleting tasks from Talend Data Stewardship
• Populating campaigns dynamically using campaign IDs
• Populating tasks into the same campaign on different Talend Data Stewardship instances
• Retrieving tasks from Talend Data Stewardship
• Writing tasks in a Merging campaign
• Writing tasks in Talend Data Stewardship campaigns

tCreateTable	Creates a table for a specific type of database.

• Creating new table in a Mysql Database

tDBFSConnection	Connects to a given DBFS (Databricks Filesystem) system so that the other DBFS components can reuse the connection it creates to communicate with this DBFS.
tDBFSGet	Copies files from a given DBFS (Databricks Filesystem) system, pastes them in a user-defined directory and if needs be, renames them.
tDBFSPut	Connects to a given DBFS (Databricks Filesystem) system, copies files from an user-defined directory, pastes them in this system and if needs be, renames these files.

• Writing and reading data from Azure Data Lake Storage using Spark (Azure Databricks)
• Writing and reading data from S3 (Databricks on AWS)

tDBCDC	Extracts only the changes made to the source operational data and makes them available to the target system(s) using database CDC views.
tDBCDCOutput	Synchronizes data changes in database of the selected database type in the CDC mode.
tDBInvalidRows	Checks database rows against specific Data Quality patterns (regular expression) or Data Quality rules (business rule).
tDBValidRows	Checks database rows against Data Quality patterns (regular expression).
tDBBulkExec	Offers gains in performance while executing the Insert operations on a database.
tDBClose	Closes the transaction committed in a connected database.
tDBColumnList	Iterates on all columns of a given database table and lists column names.
tDBCommit	Validates the data processed through the Job into the connected database.
tDBConnection	Opens a connection to a database to be reused in the subsequent subJob or subJobs.
tDBInput	Extracts data from a database.
tDBLastInsertId	Obtains the primary key value of the record that was last inserted in a database table by a user.
tDBOutput	Writes, updates, makes changes or suppresses entries in a database.
tDBOutputBulk	Writes a file with columns based on the defined delimiter and the standards of the selected database type.
tDBOutputBulkExec	Executes the Insert action in a database.
tDBRollback	Cancels the transaction commit in a connected database to avoid committing part of a transaction involuntarily.
tDBRow	Executes the stated SQL query onto a database.
tDBSCD	Reflects and tracks changes in a dedicated database SCD table.
tDBSCDELT	Reflects and tracks changes in a dedicated SCD table through SQL queries.
tDBSP	Calls a database stored procedure.
tDBTableList	Lists the names of specified database tables using a SELECT statement based on a WHERE clause.
tParseRecordSet	Parses a recordset rather than individual records from a table.

tDB2BulkExec	Executes the Insert action on the provided data and gains in performance during Insert operations to a DB2 database.
tDB2Close	Closes a transaction committed in the connected DB.
tDB2Commit	Commits in one go a global transaction instead of doing that on every row or every batch and thus provides gain in performance.
tDB2Connection	Opens a connection to the specified database that can then be reused in the subsequent subJob or subJobs.
tDB2Input	Executes a DB query with a strictly defined order which must correspond to the schema definition. Then tDB2Input passes on the field list to the next component via a Row > Main link.
tDB2Output	Executes the action defined on the table and/or on the data contained in the table, based on the flow incoming from the preceding component in the Job.
tDB2Rollback	Avoids to commit part of a transaction involuntarily.
tDB2Row	Acts on the actual DB structure or on the data (although without handling data) depending on the nature of the query and the database. The SQLBuilder tool helps you write easily your SQL statements.
tDB2SP	Offers a convenient way to call the database stored procedures.

tDBFSConnection	Connects to a given DBFS (Databricks Filesystem) system so that the other DBFS components can reuse the connection it creates to communicate with this DBFS.
tDBFSGet	Copies files from a given DBFS (Databricks Filesystem) system, pastes them in a user-defined directory and if needs be, renames them.
tDBFSPut	Connects to a given DBFS (Databricks Filesystem) system, copies files from an user-defined directory, pastes them in this system and if needs be, renames these files.

• Reading data from databases through context-based dynamic connections
• Using context parameters when reading a table from a database

tFileStreamInputDelimited	Reads data continuously, row by row, to split it into fields, then sends fields defined in its schema to the next Job component, via a Row > Main link.
tFileInputDelimited	Reads a delimited file row by row to split them up into fields and then sends the fields as defined in the schema to the next component.
tFileOutputDelimited	Outputs the input data to a delimited file according to the defined schema.
tPivotToColumnsDelimited	Fine-tunes the selection of data to output.

• Reading data from a Delimited file and display the output
• Reading data from a remote file in streaming mode
• Using a pivot column to aggregate data
• Utilizing Output Stream to save filtered data to a local file
• Writing data in a delimited file

tDeltaLakeClose	Closes an active DeltaLake connection to release the occupied resources.
tDeltaLakeConnection	Opens a connection to the specified database that can then be reused in the subsequent subJob or subJobs.
tDeltaLakeInput	Extracts the latest version or a given snapshot of records from the Delta Lake layer of your Data Lake system and sends the data to the next component for further processing.
tDeltaLakeOutput	Writes records in the Delta Lake layer of your Data Lake system in the Parquet format.
tDeltaLakeRow	Acts on the actual DB structure or on the data (although without handling data) using the SQLBuilder tool to write easily your SQL statements.

• Computing the day-over-day evolution of US flights using a related Delta Lake dataset

tDotNETInstantiate	Invokes the constructor of a .NET object that is intended for later reuse.
tDotNETRow	Facilitates data transform by utilizing custom or built-in .NET classes.

• Integrating .Net into Talend Studio: Introduction
• Utilizing .NET in Talend

tDropboxConnection	Creates a Dropbox connection to a given account that the other Dropbox components can reuse.
tDropboxDelete	Removes a given folder or file from Dropbox.
tDropboxGet	Downloads a selected file from a Dropbox account to a specified local directory.
tDropboxList	Lists the files stored in a specified directory on Dropbox.
tDropboxPut	Uploads data to Dropbox from either a local file or a given data flow.

• Uploading files to Dropbox

tSetDynamicSchema	Sets a dynamic schema that can be reused by components in the subsequent subJob or subJobs to retrieve data from unknown columns.

• Handling a positional file based on a dynamic schema
• Writing dynamic columns from a database to an output file

tElasticSearchConfiguration	Enables the reuse of the connection configuration to ElasticSearch in the same Job.
tElasticSearchInput	Reads documents from a given Elasticsearch system based on a user-defined query.
tElasticSearchLookupInput	Executes a ElasticSearch query with a strictly defined order which must correspond to the schema definition.
tElasticSearchOutput	Writes datasets into a given Elasticsearch system.

tELTGreenplumInput	Adds as many Input tables as required for the most complicated Insert statement.
tELTGreenplumMap	Uses the tables provided as input to feed the parameter in the built statement. The statement can include inner or outer joins to be implemented between tables or between one table and its aliases.
tELTGreenplumOutput	Executes the SQL Insert, Update and Delete statement to the Greenplum database

• Aggregating Snowflake data using context variables as table and connection names
• Aggregating table columns and filtering
• Mapping data using a simple implicit join
• Mapping data using a subquery
• Mapping date using using an Alias table

tELTHiveInput	Replicates the schema, which the tELTHiveMap component that follows will use, of the input Hive table.
tELTHiveMap	Builds graphically the Hive QL statement in order to transform data.
tELTHiveOutput	Works alongside tELTHiveMap to write data into the Hive table.

• Aggregating Snowflake data using context variables as table and connection names
• Joining table columns and writing them into Hive
• Mapping data using a subquery

tELTInput	Adds as many Input tables as required for the SQL statement to be executed.
tELTMap	Uses the tables provided as input to feed the parameter in the built SQL statement. The statement can include inner or outer joins to be implemented between tables or between one table and its aliases.
tELTOutput	Carries out the action on the table specified and inserts the data according to the output schema defined in the ELT Mapper.

• Aggregating Snowflake data using context variables as table and connection names
• Aggregating table columns and filtering
• Mapping data using a simple implicit join
• Mapping data using a subquery
• Mapping date using using an Alias table

tELTMSSqlInput	Adds as many Input tables as required for the most complicated Insert statement.
tELTMSSqlMap	Uses the tables provided as input to feed the parameter in the built statement. The statement can include inner or outer joins to be implemented between tables or between one table and its aliases.
tELTMSSqlOutput	Executes the SQL Insert, Update and Delete statement to the MSSql database

• Aggregating Snowflake data using context variables as table and connection names
• Aggregating table columns and filtering
• Mapping data using a simple implicit join
• Mapping data using a subquery
• Mapping date using using an Alias table

tELTMysqlInput	Adds as many Input tables as required for the most complicated Insert statement.
tELTMysqlMap	Uses the tables provided as input to feed the parameter in the built statement. The statement can include inner or outer joins to be implemented between tables or between one table and its aliases.
tELTMysqlOutput	tELTMysqlOutput executes the SQL Insert, Update and Delete statement to the Mysql database

• Aggregating Snowflake data using context variables as table and connection names
• Aggregating table columns and filtering
• Mapping data using a simple implicit join
• Mapping data using a subquery
• Mapping date using using an Alias table

tELTNetezzaInput	Allows you to add as many Input tables as required for the most complicated Insert statement.
tELTNetezzaMap	Uses the tables provided as input, to feed the parameter in the built statement. The statement can include inner or outer joins to be implemented between tables or between one table and its aliases.
tELTNetezzaOutput	Performs the action (insert, update or delete) on data in the specified Netezza table through the SQL statement generated by the tELTNetezzaMap component.

• Aggregating Snowflake data using context variables as table and connection names
• Aggregating table columns and filtering
• Mapping data using a simple implicit join
• Mapping data using a subquery
• Mapping date using using an Alias table

tELTOracleInput	Provides the Oracle table schema that will be used by the tELTOracleMap component to generate the SQL SELECT statement.
tELTOracleMap	Builds the SQL SELECT statement using the table schema(s) provided by one or more tELTOracleInput components.
tELTOracleOutput	Performs the action (insert, update, delete, or merge) on data in the specified Oracle table through the SQL statement generated by the tELTOracleMap component.

• Aggregating Snowflake data using context variables as table and connection names
• Aggregating table columns and filtering
• Mapping data using a simple implicit join
• Mapping data using a subquery
• Mapping date using using an Alias table
• Updating Oracle database entries
• Managing data using the Oracle MERGE function

tELTPostgresqlInput	Provides the Postgresql table schema that will be used by the tELTPostgresqlMap component to generate the SQL SELECT statement.
tELTPostgresqlMap	Builds the SQL SELECT statement using the table schema(s) provided by one or more tELTPostgresqlInput components.
tELTPostgresqlOutput	Performs the action (insert, update or delete) on data in the specified Postgresql table through the SQL statement generated by the tELTPostgresqlMap component.

• Aggregating Snowflake data using context variables as table and connection names
• Aggregating table columns and filtering
• Mapping data using a simple implicit join
• Mapping data using a subquery
• Mapping date using using an Alias table

tELTSybaseInput	Provides the Sybase table schema that will be used by the tELTSybaseMap component to generate the SQL SELECT statement.
tELTSybaseMap	Builds the SQL SELECT statement using the table schema(s) provided by one or more tELTSybaseInput components.
tELTSybaseOutput	Performs the action (insert, update or delete) on data in the specified Sybase table through the SQL statement generated by the tELTSybaseMap component.

• Aggregating Snowflake data using context variables as table and connection names
• Aggregating table columns and filtering
• Mapping data using a simple implicit join
• Mapping date using using an Alias table

tELTTeradataInput	Provides the Teradata table schema that will be used by the tELTTeradataMap component to generate the SQL SELECT statement.
tELTTeradataMap	Builds the SQL SELECT statement using the table schema(s) provided by one or more tELTTeradataInput components.
tELTTeradataOutput	Performs the action (insert, update or delete) on data in the specified Teradata table through the SQL statement generated by the tELTTeradataMap component.

• Aggregating Snowflake data using context variables as table and connection names
• Aggregating table columns and filtering
• Mapping data using a simple implicit join
• Mapping data using a subquery
• Mapping date using using an Alias table

tELTVerticaInput	Provides the Vertica table schema that will be used by the tELTVerticaMap component to generate the SQL SELECT statement.
tELTVerticaMap	Builds the SQL SELECT statement using the table schema(s) provided by one or more tELTVerticaInput components.
tELTVerticaOutput	Performs the action (insert, update or delete) on data in the specified Vertica table through the SQL statement generated by the tELTVerticaMap component.

• Aggregating Snowflake data using context variables as table and connection names
• Aggregating table columns and filtering
• Mapping data using a simple implicit join
• Mapping data using a subquery
• Mapping date using using an Alias table

tRESTClient	Interacts with RESTful Web service providers by sending HTTP and HTTPS requests using CXF (JAX-RS) getting the corresponding responses.
tRESTRequest	Receives GET/POST/PUT/PATCH/DELETE requests from the clients on the server end.
tRESTResponse	Returns a specific HTTP status code to the client end as a response to the HTTP and/or HTTP requests.

• Building a JSON document with tXMLMap to call a REST service
• Getting user information by interacting with a RESTful service
• Using a REST service to accept HTTP POST requests
• Using a REST service to accept HTTP POST requests and send responses
• Using a REST service to accept HTTP POST requests in an HTML form
• Updating user information by interacting with a RESTful service
• Using URI Query parameters to explore the data of a database
• Using a REST service to accept HTTP GET requests and send responses
• Using context variables in REST endpoint URLs in Data Services

tESBConsumer	Calls the defined method from the invoked Web service and returns the class as defined, based on the given parameters.
tESBProviderFault	Serves a Talend Job cycle result as a Fault message of the Web service in case of a request response communication style.
tESBProviderRequest	Wraps Talend Job as web service.
tESBProviderResponse	Serves a Talend Job cycle result as a response message.

• Requesting airport names based on country codes
• Returning Hello world response
• Sending a message without expecting a response
• Using tESBConsumer to retrieve the valid email
• Using tESBConsumer with custom SOAP Headers

tEXABulkExec	Imports data into an Exasol database table using the IMPORT command provided by the Exasol database in a fast way.
tEXAClose	Closes an active connection to an Exasol database instance to release the occupied resources.
tEXACommit	Validates the data processed through the Job into the connected Exasol database.
tEXAConnection	Opens a connection to an Exasol database instance that can then be reused by other Exasol components.
tEXAInput	Retrieves data from an Exasol database based on a query with a strictly defined order which corresponds to the schema definition, and passes the data to the next component.
tEXAOutput	Writes, updates, modifies or deletes data in an Exasol database by executing the action defined on the table and/or on the data in the table, based on the flow incoming from the preceding component.
tEXARollback	Cancels the transaction commit in the connected Exasol database.
tEXARow	Executes SQL queries on an Exasol database.

• Importing data into an Exasol database table from a local CSV file

tFileInputExcel	Reads an Excel file row by row to split them up into fields using regular expressions and then sends the fields as defined in the schema to the next component.
tFileOutputExcel	Writes an MS Excel file with separated data values according to a defined schema.

• Extracting data from specific Excel cells

tEXistConnection (deprecated)	Opens a connection to an eXist database in order that a transaction may be carried out.
tEXistDelete (deprecated)	Deletes specified resources from a remote eXist database.
tEXistGet (deprecated)	Retrieves selected resources from a remote eXist database to a defined local directory.
tEXistList (deprecated)	Lists the resources stored on a remote eXist database.
tEXistPut (deprecated)	Uploads specified files from a defined local directory to a remote eXist database.
tEXistXQuery (deprecated)	Queries XML files located on remote databases using local files containing XPath queries and outputs the results to an XML file stored locally.
tEXistXUpdate (deprecated)	Processes XML file records and updates the existing records on the database server.

• Retrieving resources from a remote eXist DB server

tFirebirdClose	Closes a transaction with a Firebird database.
tFirebirdCommit	Commits a global transaction instead of doing so on every row or every batch, thus providing a gain in performance.
tFirebirdConnection	Opens a connection to the specified database that can then be reused in the subsequent subJob or subJobs.
tFirebirdInput	Executes a database query on a Firebird database with a strictly defined order which must correspond to the schema definition then passes on the field list to the next component via a Main row link.
tFirebirdOutput	Executes the action defined on the table in a Firebird database and/or on the data contained in the table, based on the flow incoming from the preceding component in the Job.
tFirebirdRollback	Cancels the transation committed in the connected Firebird database.
tFirebirdRow	Executes the stated SQL query on the specified Firebird database.

tFlumeInput	Acts as interface to integrate Flume and the Spark Streaming Job developed with the Studio to continuously read data from a given Flume agent.
tFlumeOutput	Acts as interface to integrate Flume and the Spark Streaming Job developed with the Studio to continuously send data to a given Flume agent.

tFTPClose	Closes an active FTP connection to release the occupied resources.
tFTPConnection	Opens an FTP connection to transfer files in a single transaction.
tFTPDelete	Deletes files or folders in a specified directory on an FTP server.
tFTPFileExist	Checks if a file or a directory exists on an FTP server.
tFTPFileList	Lists all files and folders directly under a specified directory based on a filemask pattern.
tFTPFileProperties	Retrieves the properties of a specified file on an FTP server.
tFTPGet	Downloads files to a local directory from an FTP directory.
tFTPPut	Uploads files from a local directory to an FTP directory.
tFTPRename	Renames files in an FTP directory.
tFTPTruncate	Truncates files in an FTP directory.

• Listing and getting files/folders on an FTP directory
• Putting files onto an FTP server
• Renaming a file located on an FTP server

tFileStreamInputFullRow	Reads data in a newly-created file row by row and sends the entire rows within one single field to the next Job component, via a Row > Main link.
tFileInputFullRow	Reads a file row by row and sends complete rows of data as defined in the schema to the next component via a Row link.

• Reading full rows in a delimited file

tGlobalVarLoad	Sets variables using the incoming data so that the data can be dynamically reused by other subJobs.
tSetGlobalVar	Facilitates the process of defining global variables.

• Selecting the salary records above the average using a Map/Reduce Job
• Printing out the content of a global variable

tBigQueryConfiguration	Provides the connection configuration to Google BigQuery and Google Cloud Storage for a Spark Job.
tBigQueryBulkExec	Transfers given data to Google BigQuery.
tBigQueryInput	Performs the queries supported by Google BigQuery.
tBigQueryOutput	Transfers the data provided by its preceding component to Google BigQuery.
tBigQueryOutputBulk	Creates a .txt or .csv file for the data of large size so that you can process it according to your needs before transferring it to Google BigQuery.
tBigQuerySQLRow	Connects to Google BigQuery and performs queries to select data from tables row by row or create or delete tables in Google BigQuery.

• Performing a query in Google BigQuery
• Writing data in Google BigQuery

tGoogleDataprocManage	Creates or deletes a Dataproc cluster in the Global region on Google Cloud Platform.

tGoogleDriveConnection	Opens a Google Drive connection that can be reused by other Google Drive components.
tGoogleDriveCopy	Creates a copy of a file/folder in Google Drive.
tGoogleDriveCreate	Creates a new folder in Google Drive.
tGoogleDriveDelete	Deletes a file/folder in Google Drive.
tGoogleDriveGet	Gets a file's content and downloads the file to a local directory.
tGoogleDriveList	Lists all files, or folders, or both files and folders in a specified Google Drive folder, in the domain, including both Shared Drive and My Drive, and all shared drives.
tGoogleDrivePut	Uploads data from a data flow or a local file to Google Drive.

• Managing files with Google Drive

tPubSubInput	Connects to the Google Cloud PubSub service that transmits messages to the components that run transformations over these messages.
tPubSubInputAvro	Connects to Google Cloud Pub/Sub to receive messages in the Avro format for the components that run transformations over these messages.
tPubSubOutput	Receives messages serialized into byte arrays by its preceding component and issues these messages into a given PubSub service.

tGPGDecrypt	Calls the gpg -d command to decrypt a GnuPG-encrypted file and saves the decrypted file in the specified directory.

• Decrypting a GnuPG-encrypted file and display its content

tGreenplumBulkExec	Improves performance when loading data in a Greenplum database.
tGreenplumClose	Closes a connection to the Greenplum database.
tGreenplumCommit	Commits global transaction in one go instead of repeating the operation for every row or every batch and thus provides gain in performance.
tGreenplumConnection	Opens a connection to the specified database that can then be reused in the subsequent subJob or subJobs.
tGreenplumGPLoad	Bulk loads data into a Greenplum table either from an existing data file, an input flow, or directly from a data flow in streaming mode through a named-pipe.
tGreenplumInput	Reads a database and extracts fields based on a query.
tGreenplumOutput	Executes the action defined on the table and/or on the data of a table, according to the input flow from the previous component.
tGreenplumOutputBulk	Prepares the file to be used as parameter in the INSERT query to feed the Greenplum database.
tGreenplumOutputBulkExec	Provides performance gains during Insert operations to a Greenplum database.
tGreenplumRollback	Avoids to commit part of a transaction involuntarily.
tGreenplumRow	Acts on the actual DB structure or on the data (although without handling data), depending on the nature of the query and the database.

tGroovy	tGroovy broadens the functionality if the Job, using the Groovy language which is a simplified Java syntax.
tGroovyFile	Broadens the functionality of Jobs using the Groovy language which is a simplified Java syntax.

• Calling a file which contains Groovy code

tGoogleCloudConfiguration	Provides the connection configuration to Google Cloud Platform for a Spark Job.
tGSConfiguration	Provides the connection configuration to Google Cloud Storage for a Spark Job.
tGSBucketCreate	Creates a new bucket which you can use to organize data and control access to data in Google Cloud Storage.
tGSBucketDelete	Deletes an empty bucket in Google Cloud Storage so as to release occupied resources.
tGSBucketExist	Checks the existence of a bucket in Google Cloud Storage so as to make further operations.
tGSBucketList	Retrieves a list of buckets from all projects or one specific project in Google Cloud Storage.
tGSClose	Closes an active connection to Google Cloud Storage in order to release the occupied resources.
tGSConnection	Provides the authentication information for making requests to the Google Cloud Storage system and enables the reuse of the connection it creates to Google Cloud Storage.
tGSCopy	Copies or moves objects within a bucket or between buckets in Google Cloud Storage.
tGSDelete	Deletes the objects which match the specified criteria in Google Cloud Storage so as to release the occupied resources.
tGSGet	Retrieves objects which match the specified criteria from Google Cloud Storage and outputs them to a local directory.
tGSList	Retrieves a list of objects from Google Cloud Storage one by one.
tGSPut	Uploads files from a local directory to Google Cloud Storage so that you can manage them with Google Cloud Storage.

• Managing files with Google Cloud Storage

tHBaseConfiguration	Enables the reuse of the connection configuration to HBase in the same Job.
tHBaseLookupInput	Provides lookup data to the main flow of a streaming Job.
tHBaseClose	Closes an HBase connection you have established in your Job.
tHBaseConnection	Establishes an HBase connection to be reused by other HBase components in your Job.
tHBaseInput	Reads data from a given HBase database and extracts columns of selection.
tHBaseOutput	Writes columns of data into a given HBase database.

• Exchanging customer data with HBase

tHCatalogInput	Reads data from an HCatalog managed Hive database and send data to the component that follows.
tHCatalogLoad	Reads data directly from HDFS and writes this data into an established HCatalog managed table.
tHCatalogOperation	Prepares the HCatalog managed database/table/partition to be processed.
tHCatalogOutput	Receives data from its incoming flow and writes this data into an HCatalog managed table.

• Managing HCatalog tables on Hortonworks Data Platform

tHDFSConfiguration	Enables the reuse of the connection configuration to HDFS in the same Job.
tHDFSCompare	Compares two files in HDFS and based on the read-only schema, generates a row flow that presents the comparison information.
tHDFSConnection	Connects to a given HDFS so that the other Hadoop components can reuse the connection it creates to communicate with this HDFS.
tHDFSCopy	Copies a source file or folder into a target directory in HDFS and removes this source if required.
tHDFSDelete	Deletes a file located on a given Hadoop distributed file system (HDFS).
tHDFSExist	Checks whether a file exists in a specific directory in HDFS.
tHDFSGet	Copies files from Hadoop distributed file system(HDFS), pastes them in a user-defined directory and if needs be, renames them.
tHDFSInput	Extracts the data in a HDFS file for other components to process it.
tHDFSList	tHDFSList retrieves a list of files or folders based on a filemask pattern and iterates on each unity.
tHDFSOutput	Writes data flows it receives into a given Hadoop distributed file system (HDFS).
tHDFSOutputRaw	Transfers data of different formats such as hierarchical data in the form of a single column into a given HDFS file system.
tHDFSProperties	Creates a single row flow that displays the properties of a file processed in HDFS.
tHDFSPut	Connects to Hadoop distributed file system to load large-scale files into it with optimized performance.
tHDFSRename	Renames the selected files or specified directory on HDFS.
tHDFSRowCount	Reads a file in HDFS row by row in order to determine the number of rows this file contains.

• Checking the existence of a file in HDFS
• Computing data with Hadoop distributed file system
• Using HDFS components to work with Azure Data Lake Storage (ADLS)
• Iterating on a HDFS directory

tHiveClose	Closes connection to a Hive database.
tHiveConfiguration	Enables the reuse of the connection configuration to Hive in the same Job.
tHiveConnection	Establishes a Hive connection to be reused by other Hive components in your Job.
tHiveCreateTable	Creates Hive tables that fit a wide range of Hive data formats.
tHiveInput	Extracts data from Hive and sends the data to the component that follows.
tHiveLoad	Writes data of different formats into a given Hive table or to export data from a Hive table to a directory.
tHiveOutput	Connects to a given Hive database and writes the data it receives into a given Hive table or a directory in HDFS.
tHiveRow	Acts on the actual DB structure or on the data without handling data itself, depending on the nature of the query and the database.
tHiveWarehouseConfiguration	Enables the reuse of the Hive Warehouse Connector connection configuration to Hive in the same Job.
tHiveWarehouseInput	Extracts data from Hive and sends the data to the component that follows using Hive Warehouse Connector.
tHiveWarehouseOutput	Connects to a given Hive database and writes the received data into a given Hive table or a directory in HDFS using Hive Warehouse Connector.

• Creating a JDBC Connection to Azure HDInsight Hive
• Creating a partitioned Hive table

tHSQLDbInput	Executes a DB query with a strictly defined order which must correspond to the schema definition and then it passes on the field list to the next component via a Main row link.
tHSQLDbOutput	Executes the action defined on the table and/or on the data contained in the table, based on the flow incoming from the preceding component in the Job.
tHSQLDbRow	Acts on the actual DB structure or on the data (although without handling data), depending on the nature of the query and the database.

tHttpRequest	Sends an HTTP request to the server and outputs the response information locally.

• Sending a HTTP request to the server and saving the response information to a local file
• Sending a POST request from a local JSON file

tImpalaClose	Closes connection to an Impala database.
tImpalaConnection	Establishes an Impala connection to be reused by other Impala components in your Job.
tImpalaCreateTable	Creates Impala tables that fit a wide range of Impala data formats.
tImpalaInput	Executes the select queries to extract the corresponding data and sends the data to the component that follows.
tImpalaLoad	Writes data of different formats into a given Impala table or to export data from an Impala table to a directory.
tImpalaOutput	Executes the action defined on the data contained in the table, based on the flow incoming from the preceding component in the Job.
tImpalaRow	Acts on the actual DB structure or on the data (although without handling data).

tInformixBulkExec	Executes Insert operations in Informix databases.
tInformixClose	Closes connection to Informix databases.
tInformixCommit	Makes a global commit just once instead of commiting every row or batch of rows separately.
tInformixConnection	Opens a connection to the specified database that can then be reused in the subsequent subJob or subJobs.
tInformixInput	Reads a database and extracts fields based on a query.
tInformixOutput	Executes the action defined on the table and/or on the data contained in the table, based on the flow incoming from the preceding component in the Job.
tInformixOutputBulk	Prepares the file to be used as a parameter in the INSERT query used to feed Informix databases.
tInformixOutputBulkExec	Carries out Insert operations in Informix databases using the data provided.
tInformixRollback	Prevents involuntary transaction commits by canceling transactions in connected databases.
tInformixRow	Acts on the actual DB structure or on the data (although without handling data) thanks to the SQLBuilder that helps you write easily your SQL statements.
tInformixSP	Centralises and calls multiple and complex queries in a database.

tIngresBulkExec (deprecated)	Inserts data in bulk to a table in the Ingres DBMS for performance gain.
tIngresClose (deprecated)	Closes the transaction committed in the connected Ingres database.
tIngresCommit (deprecated)	Commits in one go, using a unique connection, a global transaction instead of doing that on every row or every batch and thus provides gain in performance.
tIngresConnection (deprecated)	Opens a connection to the specified database that can then be reused in the subsequent subJob or subJobs.
tIngresInput (deprecated)	Reads an Ingres database and extracts fields based on a query.
tIngresOutput (deprecated)	Executes the action defined on the table and/or on the data contained in the table, based on the flow incoming from the preceding component in the Job.
tIngresOutputBulk (deprecated)	Prepares the file whose data is inserted in bulk to the Ingres DBMS for performance gain.
tIngresOutputBulkExec (deprecated)	Inserts data in bulk to a table in the Ingres DBMS for performance gain.
tIngresRollback (deprecated)	Avoids to commit part of a transaction involuntarily by canceling the transaction committed in the connected database.
tIngresRow (deprecated)	Acts on the actual DB structure or on the data (although without handling data) using the SQLBuilder tool to write easily your SQL statements.

• Loading data to a table in the Ingres DBMS

tInterbaseClose (deprecated)	Closes the transaction committed in the connected Interbase database.
tInterbaseCommit (deprecated)	Commits in one go a global transaction instead of doing that on every row or every batch and thus provides gain in performance.
tInterbaseConnection (deprecated)	Opens a connection to the specified database that can then be reused in the subsequent subJob or subJobs.
tInterbaseInput (deprecated)	Reads an Interbase database and extracts fields based on a query.
tInterbaseOutput (deprecated)	Executes the action defined on the table and/or on the data contained in the table, based on the flow incoming from the preceding component in the Job.
tInterbaseRollback (deprecated)	Avoids to commit part of a transaction involuntarily by canceling the transaction committed in the connected Interbase database.
tInterbaseRow (deprecated)	Acts on the actual database structure or on the data (although without handling data) using the SQLBuilder tool to write easily your SQL statements.

tFileFetch	Retrieves a file through the given protocol (HTTP, HTTPS, FTP, or SMB).

• Fetching data through HTTP
• Reusing stored cookie to fetch files through HTTP

tJasperOutput	Creates a report in rich formats using Jaspersoft's iReport.
tJasperOutputExec	Creates a report in rich formats using Jaspersoft's iReport and offers a performance gain as it functions as a combination of an input component and a tJasperOutput component.

• Generating a report against a .jrxml template

tJavaMR	Provides an editor that enables you to enter personalized MapReduce code in order to integrate it in Talend program.

• Counting words using custom map and reduce code (deprecated)

tJavaStorm (deprecated)	Provides a Java code editor that lets you enter the custom Storm code you want to use in the Storm topology you are designing.

• Analyzing people's activities using a Storm topology (deprecated)

tJava	Extends the functionalities of a Talend Job using custom Java commands.
tJavaFlex	Provides a Java code editor that lets you enter personalized code in order to integrate it in Talend program.
tJavaRow	Provides a code editor that lets you enter the Java code to be applied to each row of the flow.

• Using tJavaFlex to display file content based on a dynamic schema
• Using tJavaRow to handle file content based on a dynamic schema
• Checking the format of an e-mail address
• Generating data flow
• Printing out a variable content
• Processing rows of data with tJavaFlex
• Redirecting the standard output to a file for the entire Job
• Transforming data line by line using tJavaRow

tJavaDBInput	Reads a database and extracts fields based on a query
tJavaDBOutput	Executes the action defined on the table and/or on the data contained in the table, based on the flow incoming from the preceding component in the Job.
tJavaDBRow	Acts on the actual database structure or on the data (although without handling data) using the SQLBuilder tool to write easily your SQL statements.

tJBossESBInput	Retrieves a message from a JBossESB server to process it as a flow that can be used in a Talend Job.
tJBossESBOutput	Transforms the data used in a Talend Job into a JBossESB message.

tJDBCConfiguration	Stores connection information and credentials to be reused by other JDBC components.
tJDBCLookupInput	Reads a database and extracts fields based on a query.
tJDBCClose	Closes an active JDBC connection to release the occupied resources.
tJDBCColumnList	Lists all column names of a given JDBC table.
tJDBCCommit	Commits in one go a global transaction instead of doing that on every row or every batch and thus provides gain in performance.
tJDBCConnection	Opens a connection to the specified database that can then be reused in the subsequent subJob or subJobs.
tJDBCInput	Reads any database using a JDBC API connection and extracts fields based on a query.
tJDBCOutput	Executes the action defined on the data contained in the table, based on the flow incoming from the preceding component in the Job.
tJDBCRollback	Avoids commiting part of a transaction accidentally by canceling the transaction committed in the connected database.
tJDBCRow	Acts on the actual DB structure or on the data (although without handling data) using the SQLBuilder tool to write easily your SQL statements.
tJDBCSP	Centralizes multiple or complex queries in a database in order to call them easily.
tJDBCTableList	Lists the names of a given set of JDBC tables using a select statement based on a Where clause.

tJIRAInput	Retrieves the issue information based on a JQL query or retrieve the project information based on a specified project ID from JIRA.
tJIRAOutput	Inserts, updates, or deletes the issue or project information in JIRA.

• Creating an issue in JIRA application
• Retrieving the project information from JIRA application
• Updating an issue in JIRA application

tJMSInput	Creates an interface between a Java application and a Message-Oriented middleware system.
tJMSOutput	Creates an interface between a Java application and a Message-Oriented middleware system.

• Enqueuing/dequeuing a message on the ActiveMQ server

tFileStreamInputJSON	Extracts JSON data from a file, then transfers the data to, for instance, a file or a database table.
tFileInputJSON	Extracts JSON data from a file and transfers the data to a file, a database table, etc.
tFileOutputJSON	Receives data and rewrites it in a JSON structured data block in an output file.

• Extracting JSON data from a URL
• Extracting JSON data from a file using JSONPath
• Extracting JSON data from a file using JSONPath without setting a loop node
• Extracting JSON data from a file using XPath
• Writing a JSON structured file

tKafkaInputAvro	Transmits Avro-formatted messages you need to process to its following component in the Job you are designing.
tKafkaCommit	Saves the current state of the tKafkaInput to which it is connected.
tKafkaConnection	Opens a reusable Kafka connection.
tKafkaCreateTopic	Creates a Kafka topic that the other Kafka components can use.
tKafkaInput	Transmits messages you need to process to the components that follow in the Job you are designing.
tKafkaOutput	Publishes messages into a Kafka system.

• Analyzing a Twitter flow in near real-time
• Analyzing people's activities using a Storm topology (deprecated)

tSetKerberosConfiguration	Sets the relevant information for Kerberos authentication.

tSetKeystore	Sets the authentication data type between PKCS 12 and JKS.

• Extracting customer information from a private WSDL file

tKinesisInput	Acts as consumer of an Amazon Kinesis stream to pull messages from this Kinesis stream.
tKinesisInputAvro	Acts as consumer of an Amazon Kinesis stream to pull messages from this Kinesis stream.
tKinesisOutput	Acts as data producer to put data to an Amazon Kinesis stream for real-time ingestion.

• Working with Amazon Kinesis and Big Data Streaming Jobs

tKuduConfiguration	Enables the reuse of the connection configuration to Cloudera Kudu in the same Job.
tKuduInput	Retrieves data from a Cloudera Kudu table and sends them to the component that follows for transformation.
tKuduOutput	Creates, updates or deletes data in a Cloudera Kudu table.

• Writing and reading data from Cloudera Kudu using a Spark Batch Job

tLDAPAttributesInput	Analyses each object found via the LDAP query and lists a collection of attributes associated with the object.
tLDAPClose	Disconnects one connection to the LDAP Directory server so as to release occupied resources.
tLDAPConnection	Creates a connection to an LDAP Directory server.
tLDAPInput	Executes an LDAP query based on the given filter and corresponding to the schema definition. Then it passes on the field list to the next component via a Row > Main link.
tLDAPOutput	Executes an LDAP query based on the given filter and corresponding to the schema definition. Then it passes on the field list to the next component via a Row > Main link.
tLDAPRenameEntry	Renames ones or more entries in a specific LDAP directory.

• Displaying LDAP directory's filtered content
• Editing data in a LDAP directory

tFileInputLDIF	Reads an LDIF file row by row to split them up into fields and sends the fields as defined in the schema to the next component using a Row connection.
tFileOutputLDIF	Writes or modifies an LDIF file with data separated in respective entries based on the schema defined, or else deletes content from an LDIF file.

• Writing data from a database table into an LDIF file

tLibraryLoad	Loads useable Java libraries in a Job.

• Checking the format of an e-mail address

tAssert	Generates the boolean evaluation on the concern for the Job execution status and provides the Job status messages to tAssertCatcher.
tAssertCatcher	Generates a data flow consolidating the status information of a job execution and transfer the data into defined output files.
tChronometerStart	Operates as a chronometer device that starts calculating the processing time of one or more subJobs in the main Job, or that starts calculating the processing time of part of your subJob.
tChronometerStop	Operates as a chronometer device that stops calculating the processing time of one or more subJobs in the main Job, or that stops calculating the processing time of part of your subJob. tChronometerStop displays the total execution time.
tDie	Triggers the tLogCatcher component for exhaustive log before killing the Job.
tFlowMeter	Counts the number of rows processed in the defined flow, so this number can be caught by the tFlowMeterCatcher component for logging purposes.
tFlowMeterCatcher	Operates as a log function triggered by the use of a tFlowMeter component in the Job.
tLogCatcher	Operates as a log function triggered by one of the three: Java exception, tDie or tWarn, to collect and transfer log data.
tLogRow	Displays data or results in the Run console to monitor data processed.
tStatCatcher	Gathers the Job processing metadata at the Job level and at the component level and transfers the log data to the subsequent component for display or storage.
tWarn	Triggers a warning often caught by the tLogCatcher component for exhaustive log.

• Catching flow metrics from a Job
• Catching messages triggered by a tWarn component
• Catching the message triggered by a tDie component
• Displaying the statistics log of Job execution
• Measuring the processing time of a subJob and part of a subJob
• Setting up the assertive condition for a Job execution
• Viewing product orders status (on a daily basis) against a benchmark number

tALSModel	Generates an user-ranking-product associated matrix, based on given user-product interactive data.
tClassify	Predicts which class an element belongs to, based on the classifier model generated by a model training component.
tClassifySVM	Predicts which class an element belongs to, based on the classifier model generated by tSVMModel.
tDecisionTreeModel	Analyzes feature vectors usually prepared and provided by tModelEncoder to generate a classifier model that is used by tPredict to classify given elements.
tGradientBoostedTreeModel	Analyzes feature vectors usually prepared and provided by tModelEncoder to generate a classifier model that is used by tPredict to classify given elements.
tKMeansModel	Analyzes incoming datasets based on applying the K-Means algorithm.
tKMeansStrModel	Analyzes incoming datasets in near real-time, based on applying the K-Means algorithm.
tLinearRegressionModel	Builds a linear regression model using a training dataset.
tLogisticRegressionModel	Analyzes feature vectors usually pre-processed by tModelEncoder to generate a classifier model that is used by tPredict to classify given elements.
tMahoutClustering (deprecated)	Groups unlabeled numerical data into clusters that can reveal interesting patterns or helps identifying abnormal data items in the data set.
tModelEncoder	Performs featurization operations to transform data into the format expected by the model training components such as tLogisticRegressionModel or tKMeansModel.
tNaiveBayesModel	Generates a classifier model that is used by tPredict to classify given elements.
tPredict	Predicts the situation of an element.
tPredictCluster	Predicts the cluster of an element.
tRandomForestModel	Analyzes feature vectors.
tRecommend	Recommends products to users known to this model, based on the user-product recommender model generated by tASLModel.
tSVMModel	Generates an SVM-based classifier model that can be used by tPredict to classify given elements.

• Creating a classification model to filter spam
• Grouping customer numerical data into clusters on HDFS (deprecated)
• Modeling the accident-prone areas in a city

tFileInputMail	Reads the standard key data of a given MIME or MSG email file.
tSendMail	Notifies recipients about a particular state of a Job or possible errors.

• Extracting key fields from an email
• Retrieving emails and extracting data from email files
• Sending an email on error
• Sending an email with attachment in HTML format

tMapRDBConfiguration	Stores connection information and credentials to be reused by other MapRDB components.
tMapRDBLookupInput	Provides lookup data to the main flow of a streaming Job.
tMapRDBClose	Closes an MapRDB connection you have established in a same Job.
tMapRDBConnection	Establishes a MapRDB connection to be reused by other MapRDB components in a same Job.
tMapRDBInput	Reads data from a given MapRDB database and extracts columns of selection.
tMapRDBOutput	Writes columns of data into a given MapRDB database.
tMapROjaiInput	Reads documents from a MapR-DB database to load the data in a given Job.
tMapROjaiOutput	Inserts, replaces or deletes documents in a MapR-DB database to be used as document database, based on the incoming flow from the preceding component in the Job.

• Writing candidate data in a MapR-DB OJAI database

tMapRStreamsInputAvro	Transmits messages in the Avro format to the Job that runs transformations over these messages. Only MapR V5.2 onwards is supported by this component.
tMapRStreamsCommit	Connects to a given tMapRStreamsInput to perform a consumer offset commit.
tMapRStreamsConnection	Opens a reusable connection to a given MapR Streams cluster so that the other MapR Streams components can reuse this connection.
tMapRStreamsCreateStream	Creates a MapR Streams stream or topic that the other MapR Streams components can use.
tMapRStreamsInput	Transmits messages to the Job that runs transformations over these messages. Only MapR V5.2 onwards is supported by this component.
tMapRStreamsOutput	Publishes messages into a MapR Streams system. Only MapR V5.2 onwards is supported by this component.

tMarketoBulkExec	Imports leads or custom objects into Marketo from a local file in the REST API mode.
tMarketoCampaign	Retrieves campaign records, activity and campaign changes related data from Marketo.
tMarketoConnection	Opens a connection to Marketo that can then be reused by other Marketo components.
tMarketoInput	Retrieves lead records, activity history, lead changes, and custom object related data from Marketo.
tMarketoListOperation	Adds/removes one or more leads to/from a list in Marketo. Also, it helps you verify the existence of one or more leads in a list in Marketo.
tMarketoOutput	Writes lead records or custom object records from the incoming data flow into Marketo.

• Adding a lead record to a Marketo list using SOAP API
• Transmitting data with Marketo using REST API

tMarkLogicBulkLoad	Imports local files into a MarkLogic server database in bulk mode using the MarkLogic Content Pump (MLCP) tool.
tMarkLogicClose	Closes an active connection to a MarkLogic database to release the occupied resources.
tMarkLogicConnection	Opens a connection to a MarkLogic database that can then be reused by other MarkLogic components.
tMarkLogicInput	Searches document content in a MarkLogic database based on a string query.
tMarkLogicOutput	Creates, updates or deletes document content in a MarkLogic database.

tMaxDBInput	Reads a database and extracts fields based on a query.
tMaxDBOutput	Writes, updates, makes changes or suppresses entries in a database.
tMaxDBRow	Acts on the actual DB structure or on the data (although without handling data), depending on the nature of the query and the database.

tMDMClose	Terminates an open MDM server connection after the execution of the proceeding subJob.
tMDMCommit	Commits all changes to the database made within the scope of a transaction in MDM.
tMDMConnection	Opens an MDM server connection for convenient reuse in the current Job or transaction.
tMDMRollback	Rolls back any changes made in the database rather than definitively committing them, for example to prevent partial commits if an error occurs.

tMDMBulkLoad	Uses bulk mode to write XML structured master data into the MDM server.
tMDMDelete	Deletes master data records from specific entities in the MDM Hub.
tMDMInput	Reads data in an MDM Hub and thus makes it possible to process this data.
tMDMOutput	Writes data into or removes data from the MDM server.
tMDMRestInput	Reads data through the REST API from the MDM Hub for further processing.
tMDMSP	Offers a convenient way to centralize multiple or complex queries in an MDM Hub and calls the stored procedure easily.
tMDMViewSearch	Retrieves the MDM records from an MDM hub by applying filtering criteria you have created in a specific view and puts out results in XML structure.

• Deleting master data from an MDM Hub
• Executing a stored procedure using tMDMSP
• Loading records into a business entity
• Reading data from an MDM hub through the REST API
• Reading master data from an MDM hub
• Reading staging data from MDM
• Removing master data partially from the MDM hub
• Retrieving records from an MDM hub via an existing view
• Writing master data in an MDM hub
• Writing staging data into MDM

tMDMReceive	Decodes a context parameter holding MDM XML data and transforms it into a flat schema.
tMDMRouteRecord	Helps Event Manager to identify the changes you have made on your data so that correlative actions can be triggered.
tMDMTriggerInput	Reads the XML message (Document type) sent by MDM and passes the information to the component that follows.
tMDMTriggerOutput	Receives an XML flow (Document type) from the preceding component in the Job.

• Exchanging the event information about an MDM record
• Extracting information from an MDM record in XML
• Routing an update report record to Event Manager

tMemSQLClose (deprecated)	Closes the transaction committed in the MemSQL database.
tMemSQLConnection (deprecated)	Opens a connection to the specified database that can then be reused in the subsequent subJob or subJobs.
tMemSQLInput (deprecated)	Executes a DB query with a strictly defined order which must correspond to the schema definition.
tMemSQLOutput (deprecated)	Reads data incoming from the preceding component in the Job and executes the action defined on a given MemSQL table and/or on the data contained in the table.
tMemSQLRow (deprecated)	Acts on the actual database structure or on the data (although without handling data).

• Writing data to and reading data from a MemSQL database table

tMicrosoftCrmInput	Extracts data from a Microsoft Dynamics CRM or a Microsoft Dynamics 365 CRM database based on conditions set on specific columns.
tMicrosoftCrmOutput	Writes data into a Microsoft Dynamics CRM database or a Microsoft Dynamics 365 CRM database.

• Writing data in a Microsoft Dynamics CRM database and putting conditions on columns to extract specified rows

tMicrosoftMQInput	Retrieves the first message in a given Microsoft message queue (only support String).
tMicrosoftMQOutput	Writes a defined column of given inflow data to Microsoft message queue (only support String type).

• Writing and fetching queuing messages from Microsoft message queue

tMomCommit	Commits data on the MQ Server.
tMomConnection	Opens a connection to the MQ Server for communication.
tMomInput	Fetches a message from a queue on a Message-Oriented Middleware (MOM) system and passes it on to the next component.
tMomMessageIdList	Fetches a message ID list from a queue on a Message-Oriented middleware system and passes it to the next component.
tMomOutput	Adds a message to a Message-Oriented Middleware system queue in order for it to be fetched asynchronously.
tMomRollback	Cancels the transaction committed in the MQ Server.

• Asynchronous communication via a MOM server
• Transmitting XML files via a MOM server

tMondrianInput (deprecated)	Executes a multi-dimensional expression (MDX) query corresponding to the dataset structure and schema definition.

• Extracting multi-dimenstional datasets from a MySQL database (Cross-join tables)

tMongoDBConfiguration	Stores connection information and credentials to be reused by other MongoDB components.
tMongoDBLookupInput	Executes a database query with a strictly defined order which must correspond to the schema definition.
tMongoDBBulkLoad	Imports data files in different formats (CSV, TSV or JSON) into the specified MongoDB database so that the data can be further processed.
tMongoDBClose	Closes a connection to the MongoDB database.
tMongoDBConnection	Creates a connection to a MongoDB database and reuse that connection in other components.
tMongoDBGridFSDelete	Automates the delete action over specific files in MongoDB GridFS.
tMongoDBGridFSGet	Connects to a MongoDB GridFS system to copy files from it.
tMongoDBGridFSList	Retrieves a list of files based on a query.
tMongoDBGridFSProperties	Obtains information about the properties of given files selected based on a query.
tMongoDBGridFSPut	Connects to a MongoDB GridFS system to load files into it.
tMongoDBInput	Retrieves records from a collection in the MongoDB database and transfers them to the following component for display or storage.
tMongoDBOutput	Executes the action defined on the collection in the MongoDB database.
tMongoDBRow	Executes the commands and functions of the MongoDB database.

• Reading and writing data in MongoDB using a Spark Streaming Job
• Writing and reading data from MongoDB using a Spark Batch Job
• Creating a collection and writing data to it
• Importing data into MongoDB database
• Managing files using MongoDB GridFS
• Retrieving data from a collection by advanced queries
• Upserting records in a collection
• Using MongoDB functions to create a collection and write data to it

tMQTTInput	Acts as consumer of a MQTT topic to stream messages from this topic.
tMQTTOutput	Acts as publisher to a MQTT topic to stream messages to this topic in real time.

tFileInputMSDelimited	Reads the data structures (schemas) of a multi-structured delimited file and sends the fields as defined in the different schemas to the next components using Row connections.
tFileOutputMSDelimited	Creates a complex multi-structured delimited file, using data structures (schemas) coming from several incoming Row flows.

• Reading a multi structure delimited file

tFileInputMSPositional	Reads the data structures (schemas) of a multi-structured positional file and sends the fields as defined in the different schemas to the next components using Row connections.
tFileOutputMSPositional	Creates a complex multi-structured file, using data structures (schemas) coming from several incoming Row flows.

• Reading data from a positional file

tFileInputMSXML	Reads the data structures (schemas) of a multi-structured XML file and sends the fields as defined in the different schemas to the next components using Row connections.
tFileOutputMSXML	Creates a complex multi-structured XML file, using data structures (schemas) coming from several incoming Row flows.

• Reading a multi-structure XML file

tMSSqlBulkExec	Offers gains in performance while executing the Insert operations to a Microsoft SQL Server database.
tMSSqlClose	Closes a transaction in the MSSql databases.
tMSSqlColumnList	Lists all column names of a given MSSql table.
tMSSqlCommit	Commits in one go, using a unique connection, a global transaction instead of doing that on every row or every batch and thus provides gain in performance.
tMSSqlConnection	Opens a connection to the specified database that can then be reused in the subsequent subJob or subJobs.
tMSSqlInput	Executes a DB query with a strictly defined order which must correspond to the schema definition.
tMSSqlLastInsertId	Retrieves the last primary keys added by a user to a MSSql table.
tMSSqlOutput	Executes the action defined on the table and/or on the data contained in the table, based on the flow incoming from the preceding component in the Job.
tMSSqlOutputBulk	Prepares the file to be used as parameter in the INSERT query to feed the MSSql database.
tMSSqlOutputBulkExec	Gains in performance during Insert operations to a Microsoft SQL Server database.
tMSSqlRollback	Cancels the transaction commit in the MSSql database and thus avoids to commit part of a transaction involuntarily.
tMSSqlRow	Acts on the actual DB structure or on the data (although without handling data).
tMSSqlSP	Offers a convenient way to centralize multiple or complex queries in a database and calls them easily.
tMSSqlTableList	Lists the names of a given set of MSSql tables using a select statement based on a Where clause.

• Inserting data into a database table and extracting useful information from it
• Retrieving personal information using a stored procedure

tMysqlConfiguration	Stores connection information and credentials to be reused by other MySQL components.
tMySQLInvalidRows	Checks MySQL database rows against specific Data Quality patterns (regular expression) or Data Quality rules (business rule).
tMysqlLookupInput	Reads a MySQL database and extracts fields based on a query.
tMySQLValidRows	Checks MySQL database rows against Data Quality patterns (regular expression).
tMysqlBulkExec	Offers gains in performance while executing the Insert operations on a MySQL or Aurora database.
tMysqlClose	Closes the transaction committed in a Mysql database.
tMysqlColumnList	Iterates on all columns of a given Mysql table and lists column names.
tMysqlCommit	Commits in one go, using a unique connection, a global transaction instead of doing that on every row or every batch and thus provides gain in performance.
tMysqlConnection	Opens a connection to the specified MySQL database for reuse in the subsequent subJob or subJobs.
tMysqlInput	Executes a DB query with a strictly defined order which must correspond to the schema definition.
tMysqlLastInsertId	Obtains the primary key value of the record that was last inserted in a Mysql table by a user.
tMysqlOutput	Writes, updates, makes changes or suppresses entries in a database.
tMysqlOutputBulk	Writes a file with columns based on the defined delimiter and the MySQL or Aurora standards.
tMysqlOutputBulkExec	Executes the Insert action in the specified MySQL or Aurora database.
tMysqlRollback	Cancels the transaction commit in the connected MySQL database to avoid committing part of a transaction involuntarily.
tMysqlRow	Executes the stated SQL query on the specified MySQL database.
tMysqlSP	Calls a MySQL database stored procedure.
tMysqlTableList	Lists the names of a given set of Mysql tables using a select statement based on a Where clause.

• Checking customer table against a given DQ rule to select customer records
• Controlling the data definition language via tMysqlOutput when creating a table
• Reading email addresses from a DB table and retrieving specific data
• Updating a database table using tMysqlOutput in a Big Data Streaming Job
• Writing dynamic columns from a source file to a database
• Combining two flows for selective output
• Getting the ID for the last inserted record with tMysqlLastInsertId
• Inserting a column and altering data using tMysqlOutput
• Inserting data in bulk in MySQL database
• Inserting data in mother/daughter tables
• Inserting transformed data in MySQL database
• Iterating on DB tables and deleting their content using a user-defined SQL template
• Iterating on a DB table and listing its column names
• Removing and regenerating a MySQL table index
• Retrieving data in error with a Reject link
• Sharing a database connection between a parent Job and child Job
• Updating data using tMysqlOutput
• Using PreparedStatement objects to query data
• Using tMysqlSP to find a State Label using a stored procedure
• Writing columns from a MySQL database to an output file using tMysqlInput

tNamedPipeClose	Closes a named-pipe at the end of a process.
tNamedPipeOpen	Opens a named-pipe for writing data into it.
tNamedPipeOutput	Writes data into an existing open named-pipe.

• Writing and loading data through a named-pipe

tCompareColumns	Compares two columns to design useful features for generating a classification model.
tNLPModel	Uses an input in CoNLL format and automatically generates token-level features to create a model for classification tasks like Named Entity Recognition (NER).
tNLPPredict	Uses a classifier model generated by tNLPModel to predict and label the input text.
tNLPPreprocessing	Prepares a text sample and divides it into tokens, which can be words, numbers or punctuation marks.

• Extracting named entities using a classification model
• Generating a classification model
• Preparing a text sample to be used for learning a model

tNeo4jv4Close	Closes a connection to a Neo4j version 4.x database.
tNeo4jv4Connection	Establishes a connection to a Neo4j version 4.x database for later use.
tNeo4jv4Input	Reads data from Neo4j version 4.x and sends data in the output flow.
tNeo4jv4Output	Receives data from the preceding component and writes the data into a Neo4j version 4.x database.
tNeo4jv4Row	Executes the stated Cypher query onto the specified Neo4J version 4.x database.
tNeo4jBatchOutput	Receives data from the preceding component and writes the data into a local Neo4j database.
tNeo4jBatchOutputRelationship	Receives data from the preceding component and writes relationships in bulk into a local Neo4j database.
tNeo4jBatchSchema	Defines the schema of a local Neo4j database.
tNeo4jClose	Close an active connection to an Neo4j database in embedded mode.
tNeo4jConnection	Opens a connection to a Neo4j database to be reuse by other Neo4j components.
tNeo4jImportTool	Uses Neo4j Import Tool to create a Neo4j database and import large amounts of data in bulk from CSV files to this database.
tNeo4jInput	Reads data from Neo4j and sends data in the output flow.
tNeo4jOutput	Receives data from the preceding component and writes the data into Neo4j.
tNeo4jOutputRelationship	Receives data from the preceding component and writes relationships into Neo4j.
tNeo4jRow	Executes the stated Cypher query onto the specified Neo4J database.

• Creating nodes with a label using a Cypher query
• Importing data from a CSV file to Neo4j and creating relationships using a single Cypher query
• Importing data from a CSV file to Neo4j using a Cypher query
• Writing information of actors and movies to Neo4j with hierarchical relationship using Neo4j Batch components
• Writing data to a Neo4j database and reading specific data from it
• Writing family information to Neo4j and creating relationships
• Writing information of actors and movies to Neo4j with hierarchical relationship

tNetezzaBulkExec	Offers gains in performance while carrying out the Insert operations to a Netezza database.
tNetezzaClose	Closes the transaction committed in the connected Netazza database.
tNetezzaCommit	Validates the data processed through the Job into the connected Netezza database.
tNetezzaConnection	Opens a connection to a Netezza database to be reused in the subsequent subJob or subJobs.
tNetezzaInput	Reads a database and extracts fields from a Netezza database based on a query.
tNetezzaNzLoad	Inserts data into a Netezza database table using Netezza's nzload utility.
tNetezzaOutput	Writes, updates, makes changes or suppresses entries in a Netezza database.
tNetezzaRollback	Cancels the transaction committed in the connected Netezza database to avoid committing part of a transaction involuntarily.
tNetezzaRow	Executes the SQL query stated onto the specified Netezza database.

tNetSuiteV2019Connection	Creates a connection to a NetSuite SOAP server by leveraging NetSuite v2019 features so that other NetSuite V2019 components in the Job can reuse the connection.
tNetSuiteV2019Input	Invokes the NetSuite SOAP service and retrieves data according to the conditions you specify by leveraging NetSuite v2019 features.
tNetSuiteV2019Output	Invokes the NetSuite SOAP service and inserts, updates, or removes data on the NetSuite SOAP server by leveraging NetSuite v2019 features.
tNetsuiteConnection (deprecated)	Creates a connection to the NetSuite SOAP server so that other NetSuite components in the Job can reuse the connection.
tNetsuiteInput (deprecated)	Invokes the NetSuite SOAP service and retrieves data according to the conditions you specify.
tNetsuiteOutput (deprecated)	Invokes the NetSuite SOAP service and inserts, updates, or removes data on the NetSuite SOAP server.

• Adding a record using NetSuite V2019 components
• Handling data with NetSuite
• Removing a record using NetSuite V2019 components
• Retrieving data using NetSuite V2019 components

tOpenbravoERPInput (deprecated)	Extracts data from OpenBravoERP database according to the conditions defined in specific columns.
tOpenbravoERPOutput (deprecated)	Writes data in an OpenbravoERP database.

tOracleConfiguration	Stores connection information and credentials to be reused by other Oracle components.
tOracleInvalidRows	Checks Oracle database rows against specific Data Quality patterns (regular expression) or Data Quality rules (business rule).
tOracleLookupInput	Reads a database and extracts fields based on a query.
tOracleValidRows	Checks Oracle database rows against Data Quality patterns (regular expression).
tOracleBulkExec	Offers gains in performance during operations performed on data of an Oracle database.
tOracleClose	Closes the transaction committed in the connected Oracle database.
tOracleCommit	Validates the data processed through the Job into the connected Oracle database
tOracleConnection	Opens a connection to the specified Oracle database for reuse in the subsequent subJob or subJobs.
tOracleInput	Reads an Oracle database and extracts fields based on a query.
tOracleOutput	Writes, updates, makes changes or suppresses entries in an Oracle database.
tOracleOutputBulk	Writes a file with columns based on the defined delimiter and the Oracle standards.
tOracleOutputBulkExec	Executes the Insert action in the specified Oracle database.
tOracleRollback	Cancels the transaction commit in the connected Oracle database to avoid committing part of a transaction involuntarily.
tOracleRow	Executes the stated SQL query on the specified Oracle database.
tOracleSP	Calls an Oracle database stored procedure.
tOracleTableList	Lists the names of specified Oracle tables using a SELECT statement based on a WHERE clause.

• Checking number format using a stored procedure
• Truncating and inserting file data into an Oracle database
• Using context parameters when reading a table from an Oracle database

tFileInputORC	Extracts records from a given ORC format file and sends the data to the next component for further processing.
tFileOutputORC	Receives records from the processing component placed ahead of it and writes the records into ORC format files.

tCollector	Feeds the parallel execution processes with the threads generated by tPartitioner.
tDepartitioner	Assembles the outputs of the parallel execution processes so that tRecollector can capture those outputs.
tParallelize	Manages complex Job systems. It executes several subJobs simultaneously and synchronizes the execution of a subJob with other subJobs within the main Job.
tPartitioner	Partitions the input data before tCollector can transfer them to the parallel execution processes.
tRecollector	Outputs of the parallel execution results, depending on tDepartitioner.
tFlowToIterate	Reads data line by line from the input flow and stores the data entries in iterative global variables.
tForeach	Creates a loop on a list for an iterate link.
tInfiniteLoop	Executes a task or a Job automatically, based on a loop.
tIterateToFlow	Transforms non processable data into a processable flow.
tLoop	Executes a task or a Job automatically, based on a loop
tPostjob	Triggers a task required after the execution of a Job
tPrejob	Triggers a task required for the execution of a Job
tReplicate	Duplicates the incoming schema into two identical output flows.
tRunJob	Manages complex Job systems which need to execute one Job after another.
tSleep	Identifies possible bottlenecks using a time break in the Job for testing or tracking purpose.
tUnite	Centralizes data from various and heterogeneous sources.
tWaitForFile	Iterates on a directory and triggers the next component when the defined condition is met.
tWaitForSocket	Triggers a Job based on a defined condition.
tWaitForSqlData	Iterates on a given connection for insertion or deletion of rows and triggers a subJob when a condition linked to SQL data presence is met.

• Parallelizing/synchronizing subJobs execution
• Sorting the customer data of large size in parallel
• Calling a Job and passing the parameter needed to the called Job
• Executing a Job multiple times using a loop
• Handling files before and after the execution of a data Job
• Iterating on a list and retrieving the values
• Iterating on files and merge the content
• Passing a value from a parent Job to a child Job
• Propagating the buffered output data from the child Job to the parent Job
• Replicating a flow and sorting two identical flows respectively
• Running a list of child Jobs dynamically
• Transforming a list of files as data flow
• Transforming data flow to a list
• Waiting for a file to be created and continuing the iteration loop after a message is triggered
• Waiting for a file to be created and stopping the iteration loop after a message is triggered
• Waiting for insertion of rows in a table

tPaloCheckElements (deprecated)	Checks whether elements are present in an incoming data flow existing in a given cube.
tPaloClose (deprecated)	Closes an active connection to a Palo Server.
tPaloConnection (deprecated)	Opens a connection to a Palo Server and allows other components involved in a process to share the connection for the duration of the process.
tPaloCube (deprecated)	Performs operations on a given Palo cube.
tPaloCubeList (deprecated)	Retrieves a list of cube details from the given Palo database.
tPaloDatabase (deprecated)	Manages the databases inside a Palo server.
tPaloDatabaseList (deprecated)	Lists database names, database types, number of cubes, number of dimensions, database status and database id from a given Palo server.
tPaloDimension (deprecated)	Manages Palo dimensions, even elements inside a database.
tPaloDimensionList (deprecated)	Retrieves a list of dimension details from the given Palo database.
tPaloInputMulti (deprecated)	Retrieves the stored or calculated values in combination with the element records out of a cube.
tPaloOutput (deprecated)	Takes the input stream and writes it to a given Palo cube.
tPaloOutputMulti (deprecated)	Takes the input stream and writes it to a given Palo cube.
tPaloRule (deprecated)	Manages rules in a given cube.
tPaloRuleList (deprecated)	Lists all rules, formulas, comments, activation status, external IDs from a given cube.