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Handling Streaming Data with AWS Kinesis Data Analytics Using Java

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.
Course info
Level
Intermediate
Updated
May 11, 2021
Duration
2h 53m
Table of contents
Course Overview
Handling Streaming Data Using the Apache Flink Runtime
Monitoring Jobs Using CloudWatch
Processing Twitter Feeds Using Windowing Operations
Processing Twitter Feeds Using Join Operations
Description
Course info
Level
Intermediate
Updated
May 11, 2021
Duration
2h 53m
Description

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.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi, my name is Janani Ravi, and welcome to this course on Handling Streaming Data with AWS Kinesis Data Analytics Using Java. A little about myself, I have a master's degree in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. I currently work on my own startup, Loonycorn, a studio for high‑quality video content. Kinesis Data Analytics is a service to transform and analyze streaming data with Apache Flink and SQL using serverless technologies. In this course, you will work with live Twitter feeds to process real‑time streaming data. First, you will create a developer account on the Twitter platform and generate authentication keys and tokens to access the Twitter streaming API. You will then write code to access these tweets as streaming messages and publish them to Kinesis Data Streams, which can then be used as a source of streaming data in Kinesis Data Analytics. Next, you will run Kinesis Data Analytics applications using the Apache Flink runtime to process these tweets. You will deploy these applications using the web console, as well as the command line. You will set up write permissions and configure these applications to use cloud monitoring and logging and see how you can use log messages to debug errors in your code. Finally, you will perform a number of different processing operations on streaming tweets, windowing operations using tumbling and sliding windows, you will apply global windows with count triggers and continuous time triggers, you will implement join operations and create branching pipelines to sync some results to DynamoDB and other results to S3 buckets. When you're finished with this course, you will have the skills and knowledge to create and deploy streaming applications, which process live streams, such as Twitter messages.