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Building Data Lakes on AWS with Kafka Connect, Debezium, Apicurio Registry, and Apache Hudi
Learn how to build a near real-time transactional data lake on AWS using a combination of Open Source Software (OSS) and AWS Services
Introduction
In the following post, we will explore one possible architecture for building a near real-time transactional data lake on AWS. The data lake will be built using a combination of open source software (OSS) and fully-managed AWS services. Red Hat’s Debezium, Apache Kafka, and Kafka Connect will be used for change data capture (CDC). In addition, Apache Spark, Apache Hudi, and Hudi’s DeltaStreamer will be used to manage the data lake. To complete our architecture, we will use several fully-managed AWS services, including Amazon RDS, Amazon MKS, Amazon EKS, AWS Glue, and Amazon EMR.
Source Code
The source code, configuration files, EMR Notebook, and a list of commands shown in this post are open-sourced and available on GitHub.
Kafka
According to the Apache Kafka documentation, “Apache Kafka is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.” For this post, we will use Apache Kafka as the core of our change data capture (CDC) process. According to Wikipedia, “change data capture (CDC) is a set of software design patterns used to determine and track the data that has changed so that action can be taken using the changed data. CDC is an approach to data integration that is based on the identification, capture, and delivery of the changes made to enterprise data sources.” We will discuss CDC in greater detail later in the post.
There are several options for Apache Kafka on AWS. We will use AWS’s fully-managed Amazon Managed Streaming for Apache Kafka (Amazon MSK) service…
