The Intricacies of AWS CDC to Amazon Simple Storage Service

 

Let’s see the many intricacies of the Amazon Web Service Change Data Capture (AWS CDC) feature while building data lakes on the Amazon Simple Storage Service (S3).

When AWS CDC to S3 is carried out from a relational database that is located upstream to a data lake on S3, it is necessary to handle the data at a record level. The processing engine has to read all files, make the required changes, and complete datasets. Change data capture rewrites the files as new activities such as all inserts, updates, and deletes, in specific records from a dataset.




On the other hand, poor query performance is often the result of AWS CDC to S3. It is because when data is made available by AWS CDC to S3 in real-time, it becomes split over many small files. This problem is resolved with Apache Hudi, an advanced open-source management framework. It helps in managing data at the record level in Amazon S3, leading to the simplified creation of CDC pipelines with AWS CDC to S3. Data ingestion is made more efficient through this process.

It is possible to build a CDC pipeline from AWS CDC to S3 and use AWS DMS to capture data from an Amazon Relational Database Service for MySQL database. Additionally, with Hudi, there is no need to monitor which data is being read and processed from source database. This is because Hudi auto-manages rollback, checkpointing, and recovery, facilitating the consumption of change data.

The importance of AWS CDC to S3 is that S3 users can decide the access levels between low-cost storage and high-cost unlimited options.


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