Analyzing a Twitter flow in near real-time - 7.1

Kafka

author
Talend Documentation Team
EnrichVersion
7.1
EnrichProdName
Talend Big Data
Talend Big Data Platform
Talend Data Fabric
Talend Open Studio for Big Data
Talend Real-Time Big Data Platform
task
Data Governance > Third-party systems > Messaging components (Integration) > Kafka components
Data Quality and Preparation > Third-party systems > Messaging components (Integration) > Kafka components
Design and Development > Third-party systems > Messaging components (Integration) > Kafka components
EnrichPlatform
Talend Studio

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

For more technologies supported by Talend, see Talend components.

In this scenario, you create a Spark Streaming Job to analyze, at the end of each 15-second interval, which hashtags are most used by Twitter users when they mention Paris in their Tweets over the previous 20 seconds.

An open source third-party program is used to receive and write Twitter streams in a given Kafka topic, twitter_live for example, and the Job you design in this scenario is used to consume the Tweets from this topic.

A row of Twitter raw data with hashtags reads like the example presented at https://dev.twitter.com/overview/api/entities-in-twitter-objects#hashtags.

Before replicating this scenario, you need to ensure that your Kafka system is up and running and you have proper rights and permissions to access the Kafka topic to be used. You also need a Twitter-streaming program to transfer Twitter streams into Kafka in near real-time. Talend does not provide this kind of program but some free programs created for this purpose are available in some online communities such as Github.

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-premise 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.

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

To replicate this scenario, proceed as follows: