Working with Amazon Kinesis and Big Data Streaming Jobs - 7.3

Kinesis
Version
7.3
Language
English
Product
Talend Data Fabric
Talend Real-Time Big Data Platform
Module
Talend Studio
Content
Data Governance > Third-party systems > Messaging components (Integration) > Kinesis components
Data Quality and Preparation > Third-party systems > Messaging components (Integration) > Kinesis components
Design and Development > Third-party systems > Messaging components (Integration) > Kinesis components
Last publication date
2024-02-21

This scenario shows how to work with Amazon Kinesis and Big Data Streaming Jobs using the Spark Streaming framework.

For more technologies supported by Talend, see Talend components.

This scenario applies only to Talend Real Time Big Data Platform and Talend Data Fabric.

This example uses Talend Real-Time Big Data Platform v6.1. In addition, it uses these licensed products provided by Amazon: Amazon EC2, Amazon Kinesis, and Amazon EMR.

In this example, you will build the following Job, to read and and write data to an Amazon Kinesis stream and display results in the Console.

tHDFSConfiguration is used in this scenario by Spark to connect to the HDFS system where the jar files dependent on the Job are transferred.

In the Spark Configuration tab in the Run view, define the connection to a given Spark cluster for the whole Job. In addition, since the Job expects its dependent jar files for execution, you must specify the directory in the file system to which these jar files are transferred so that Spark can access these files:
  • Yarn mode (Yarn client or Yarn cluster):

    • When using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab.

    • When using HDInsight, specify the blob to be used for Job deployment in the Windows Azure Storage configuration area in the Spark configuration tab.

    • When using Altus, specify the S3 bucket or the Azure Data Lake Storage for Job deployment in the Spark configuration tab.
    • When using Qubole, add a tS3Configuration to your Job to write your actual business data in the S3 system with Qubole. Without tS3Configuration, this business data is written in the Qubole HDFS system and destroyed once you shut down your cluster.

    • When using on-premises distributions, use the configuration component corresponding to the file system your cluster is using. Typically, this system is HDFS and so use tHDFSConfiguration.

  • Standalone mode: use the configuration component corresponding to the file system your cluster is using, such as tHDFSConfiguration Apache Spark Batch or tS3Configuration Apache Spark Batch.

    If you are using Databricks without any configuration component present in your Job, your business data is written directly in DBFS (Databricks Filesystem).

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