Building Data Lakes on the Amazon Simple Storage Service

The Amazon Web Service Database Migration Service (AWS DMS) is a cloud-based platform that is optimized for database migration between many types of sources and targets. These include on-premises servers to the cloud, one cloud provider to another, and between databases, data warehouses, relational databases, and NoSQL databases.

This software is fully automated and migration can be initiated through a few clicks on the AWS Management Console without going through lengthy processes of installing and configuring additional applications and drivers in the source database.

The most critical reason why businesses prefer building their data lakes on the Amazon Simple Storage Service (S3) is that data in its native format, whether structured, semi-structured, or structured can be loaded into the S3 data lake and processed, resulting in real-time data analytics and faster decision-making.

Now that the intricacies of AWS and S3 have been explained, let’s check the aspects of the AWS CDC to S3. 

The Change Data Capture (CDC) activity is typically started from a relational database located upstream of a data lake on Amazon S3, data being handled at a record level. The processing engine during carrying out AWS CDC to S3 tasks reads all files, makes the required changes, and rewrites complete datasets as new files for Change Data activities such as Insert, Delete, and Update on specific records from a dataset.

For information on AWS CDC to S3, Click here.

While data movement with AWS CDC to S3 is highly optimized, there is a downside to it too. This is poor query performance that is often faced by users primarily because the data that is provided by AWS CDC to S3 in real-time is bifurcated over multiple smaller files.

However, this critical issue can be resolved too with Apache Hudi. It is an advanced open-source management framework with cutting-edge features that manage data at a record level in Amazon S3. This leads to simplification in building CDC pipelines with AWS CDC to S3, resulting in efficient data ingestion.

Why do users prefer AWS CDC to S3?

The vital point is that users of Amazon S3 have the option to select the level of access as required from high-cost unlimited storage facilities to low-cost restricted usage ones. Moreover, unlimited Batch Operations are possible with S3 while retaining all the advantages of Cloud-based Change Data Capture.

A Change Data Capture pipeline can also be created while migrating data through AWS CDC to S3. Later, the AWS Database Migration Service can be used to capture data from an Amazon Relational Database Service (RDS) for the MySQL database. Apache Hudi on Amazon EMR now helps to apply these changes to a dataset in Amazon S3 doing away with the need to monitor which data is being read and processed from the source database. It is easy to consume change data with Hudi as it automatically manages checkpointing, rollback, and recovery.