Easily capture data changes over time from your database to Data Lake using Amazon Database Migration Service (DMS).
Over my past 10 years spent in the Big Data and Analytics world, I have come to realize that capturing and processing change data sets has been a challenging area. Through all these years I have seen how CDC has evolved. Let me take you through the journey:
Year 2011–2013 — For many, Hadoop is the major Data Analytics Platform. Typically, Sqoop was used to transfer data from a given database to HDFS. This worked pretty well for full table loads. Sqoop incremental could capture inserts _as well._
But CDC is not only about inserts. Where are my updates _and _deletes?
Year 2016 — We created a strategy to capture updates and deletes using triggers on database table and write changes to a shadow table. Once changed data is captured we would Sqoop to transfer the data over to HDFS. The method involved database modifications so a lot of our clients objected to it.
Year 2015–2016 — The use of a new open source project called *Debezium *was getting strong. For several years thereafter we used this CDC tool very effectively. Initially, Debezium supported only a limited number of databases but that was enough to cover most of our use case.
Debezium is able to query the database binary log and extract changes. It published each change as a JSON document to Kafka.
Image by Author — Record before and After Image
Year 2016–Now — For AWS cloud deployments we typically use Amazon Database Migration Service (DMS). DMS can read change data sets from on-premises servers or RDS and publish it to many destinations including S3, Redshift, Kafka & Elasticsearch etc.
Let me show you how to create a sample CDC pipeline. We will start by creating an RDS database on AWS, create a sample database and finally setup Amazon DMS to perform change data capture to S3.
Let's start by downloading a sample data file
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