You can build streaming applications with Apache Flink and Apache Beam
Step 1: Download the open source libraries into your favorite IDE
You can start by downloading the open-source libraries including the AWS SDK, Apache Flink, and connectors for AWS services.
Step 2: Build a sample application using Apache Flink or Apache Beam
You write your Apache Flink application code using data streams and stream operators. Application data streams are the data structure you perform processing against using your application code. Data continuously flows from the sources into application data streams. One or more stream operators are used to define your processing on the application data streams.
Step 3: Upload your code to Kinesis Data Analytics
Once built, upload your code to Amazon Kinesis Data Analytics and the service takes care of everything required to run your real-time applications continuously including scaling automatically to match the volume and throughput of your incoming data.
It’s easy to get started with Amazon Kinesis Data Analytics Studio
Step 1: Create an Amazon Kinesis Data Analytics Studio application
You can start from the Amazon Kinesis Data Analytics, Amazon MSK, or Amazon Kinesis Data Streams console. You can also use custom connectors to connect to any other data source.
Step 2: Write code in the serverless notebook in SQL, Python, and Scala and develop Apache Flink applications quickly
You can run individual paragraphs in the note, view results in context, and use Apache Zeppelin’s built-in visualization to accelerate development. You can also use user-defined functions in your code.
Step 3: Build and deploy as a Kinesis Data Analytics streaming application
You can deploy your code as a continuously running stream processing application with a just few clicks. Your deployed application will be a Kinesis Data Analytics for Apache Flink application with durable state and autoscaling. You will also get the opportunity to change sources, destinations, logging, and monitoring levels before you productionize your code.
Get started with Amazon Kinesis Data Analytics SQL
To get started, create a new Amazon Kinesis Data Analytics application. Select the demo stream we provide as input, pick a template, and edit the SQL query. You can then view the results right there in the console or load the output into Amazon Elasticsearch Service and visualize using Kibana. Within a few minutes, you will be able to deploy a complete streaming data application.
Step 1: Configure input stream
First, go to the Amazon Kinesis Data Analytics console and select a Kinesis data stream or Kinesis Data Firehose delivery stream as input. Amazon Kinesis Data Analytics ingests the data, automatically recognizes standard data formats, and suggests a schema. You can refine this schema, or if your input data is unstructured, you can define a new schema using our intuitive schema editor.
Step 2: Write your SQL queries
Next, write your SQL queries to process the streaming data using the Amazon Kinesis Data Analytics SQL editor and built-in templates, and test it with live streaming data.
Step 3: Configure output stream
Lastly, point to the destinations where you want the results loaded. Amazon Kinesis Data Analytics integrates out-of-box with Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose so it’s easy to send processed results to Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, or your own custom destination.
Get started with Amazon Kinesis Data Analytics
Instantly get access to the AWS Free Tier.
Build your streaming application from the Amazon Kinesis Data Analytics console.