Working with Amazon Kinesis and Big Data Streaming Jobs - 7.0

Kinesis

EnrichVersion
7.0
EnrichProdName
Talend Data Fabric
Talend Real-Time Big Data Platform
EnrichPlatform
Talend Studio
task
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

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 store (technical preview) for Job deployment in the Spark configuration tab.
    • When using other 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 or tS3Configuration.