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End-to-end development lifecycle for data engineers to build a data integration pipeline using AWS Glue

AWS Big Data

You can use your preferred IDE to implement AWS resource definition using the AWS Cloud Development Kit (AWS CDK) or AWS CloudFormation , and also the business logic of AWS Glue job scripts for data integration. To learn more about how to implement your AWS Glue job scripts locally, refer to Develop and test AWS Glue version 3.0

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Modernize a legacy real-time analytics application with Amazon Managed Service for Apache Flink

AWS Big Data

Traditionally, such a legacy call center analytics platform would be built on a relational database that stores data from streaming sources. Data transformations through stored procedures and use of materialized views to curate datasets and generate insights is a known pattern with relational databases.

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Build incremental data pipelines to load transactional data changes using AWS DMS, Delta 2.0, and Amazon EMR Serverless

AWS Big Data

The Delta tables created by the EMR Serverless application are exposed through the AWS Glue Data Catalog and can be queried through Amazon Athena. Data ingestion – Steps 1 and 2 use AWS DMS, which connects to the source database and moves full and incremental data (CDC) to Amazon S3 in Parquet format. EMR Serverless version 6.9.0

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Build a data lake with Apache Flink on Amazon EMR

AWS Big Data

For example, the Flink FileSystem connector has FileSystemTableFactory to read/write data in Hadoop Distributed File System (HDFS) or Amazon Simple Storage Service (Amazon S3), the Flink HBase connector has HBase2DynamicTableFactory to read/write data in HBase, and the Flink Kafka connector has KafkaDynamicTableFactory to read/write data in Kafka.