In our quest to maximize ETL efficiency, incremental loading plays a pivotal role. But what exactly is incremental loading, and why is it so important? Why are engineers ditching full loading for incremental loading?
What is Incremental Loading?
Incremental loading is a data extraction technique where only the data that has changed since the last extraction is loaded. This contrasts with full loading, where all data is loaded every time.
Why is Incremental Loading Important?
- Efficiency: By only processing new or changed data, incremental loading significantly reduces processing time and resource consumption, leading to faster ETL cycles.
- Reduced Load: It minimizes the load on your source systems and network, making the process more scalable and less disruptive to other operations.
- Timeliness: Ensures that the most current data is available in the target system without the overhead of processing the entire dataset.
- Cost Savings: Particularly in cloud environments, where processing power and storage can incur costs, incremental loading helps save on these expenses.
How to Implement Incremental Loading with AWS?
- AWS Glue: AWS Glue provides built-in support for incremental data loading through its job bookmarks feature, which tracks processed data and ensures that only new or changed data is processed in subsequent runs. For more detailed insights, you can refer to AWS's guide on Tracking processed data using job bookmarks.
- AWS Database Migration Service (DMS): AWS DMS supports Change Data Capture (CDC) to capture changes in the source database and apply them to the target database, enabling efficient incremental loading. Here you will find more details on Build an incremental data load solution using AWS DMS checkpoints and database logs.
- Amazon Kinesis: Use Amazon Kinesis Data Streams for real-time processing and incremental loading of streaming data.
- AWS Lambda: Combine AWS Lambda with other AWS services to trigger data extraction processes based on specific events or changes, ensuring only incremental data is processed. If you have time, you can go through this article from AWS on Build an ETL service pipeline to load data incrementally from Amazon S3 to Amazon Redshift using AWS Glue.
Incremental loading is a game-changer for efficient ETL processes, ensuring your data pipelines are optimized for performance, cost, and scalability.