Kinesis Data Analytics is a service to transform and analyze streaming data with Apache Flink and SQL using serverless technologies. You'll learn to use the Amazon Kinesis Data Analytics service to process streaming data using Apache Flink runtime.
Kinesis Data Analytics is part of the Kinesis streaming platform along with Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Video streams. In this course, Handling Streaming Data with AWS Kinesis Data Analytics Using Java, you'll work with live Twitter feeds to process real-time streaming data. First, you'll create a developer account on the Twitter platform and generate authentication keys and tokens to access the Twitter streaming API. You'll then write code to access these tweets as streaming messages and publish them to Kinesis Data Streams which can be used as a source of streaming data in Kinesis Data Analytics. Next, you'll run Kinesis Data Analytics applications using the Apache Flink runtime to process tweets. You'll deploy these applications using the web console as well as the command line. You'll set up the right permissions, and configure these applications to use cloud monitoring and logging, and see how you can use log messages to debug errors in your applications. Finally, you'll perform a number of different processing operations on streaming tweets, windowing operations using tumbling and sliding windows. You'll apply global windows with count triggers, and continuous-time triggers. You'll implement join operations and create branching pipelines to sink some results to DynamoDB and other results to S3. When you're finished with this course, you'll have the skills and knowledge to create and deploy streaming applications that process live streams such as Twitter messages.
Kinesis Data Analytics is part of the Kinesis streaming platform along with Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Video streams. In this course, Handling Streaming Data with AWS Kinesis Data Analytics Using Java, you'll work with live Twitter feeds to process real-time streaming data. First, you'll create a developer account on the Twitter platform and generate authentication keys and tokens to access the Twitter streaming API. You'll then write code to access these tweets as streaming messages and publish them to Kinesis Data Streams which can be used as a source of streaming data in Kinesis Data Analytics. Next, you'll run Kinesis Data Analytics applications using the Apache Flink runtime to process tweets. You'll deploy these applications using the web console as well as the command line. You'll set up the right permissions, and configure these applications to use cloud monitoring and logging, and see how you can use log messages to debug errors in your applications. Finally, you'll perform a number of different processing operations on streaming tweets, windowing operations using tumbling and sliding windows. You'll apply global windows with count triggers, and continuous-time triggers. You'll implement join operations and create branching pipelines to sink some results to DynamoDB and other results to S3. When you're finished with this course, you'll have the skills and knowledge to create and deploy streaming applications that process live streams such as Twitter messages.
