How to Implement Change Data Capture with AWS Glue?

Updated January 10th, 2024

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Combining a change data capture tool with AWS Glue helps organizations efficiently manage data changes, ensuring real-time synchronization and reducing resource usage. This enhances decision-making and operational efficiency.

Glue’s capacity to streamline data integration aligns with the core objective of change data capture – to efficiently track and synchronize data changes across systems.

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Table of contents #

Why should you use a change data capture tool with AWS Glue?
Amazon Glue overview
What is change data capture?
Steps to implement a change data capture tool with Glue
Guidelines for effective implementation
Change data capture for Glue: Related reads

Why should you use a change data capture tool with AWS Glue? #

Implementing a change data capture tool is crucial for modern data management and processing due to several reasons. Let’s take a brief look at some of them.

Efficiency in data processing: Change data capture minimizes data transfer by capturing only changes instead of entire datasets.
Real-time data synchronization: It ensures up-to-date synchronization across various systems.
Improved resource utilization: Reduces the load on network and database resources.
Enhanced data analytics: Facilitates timely and accurate data analytics, enabling faster decision-making.

Amazon Glue overview #

AWS Glue is a serverless data integration service that simplifies the process of data preparation for analytics, machine learning, and application development. It allows users to discover, prepare, move, and integrate data from various sources.

AWS Glue provides a centralized data catalog to manage data across a wide range of sources and enables the creation, running, and monitoring of ETL pipelines. It combines major data integration capabilities like discovery, modern ETL, and centralized cataloging into one service, making it user-friendly for a diverse range of technical skill sets.

What is change data capture? #

Change Data Capture (CDC) is a technique used to automatically capture changes made to the data in a database, including insertions, updates, and deletions.

CDC effectively tracks these changes in real-time or near-real-time, enabling systems to respond quickly to data modifications. Integrating a change data capture tool with AWS Glue is highly beneficial. This combination allows for seamless data integration and transformation, facilitating efficient ETL (Extract, Transform, Load) processes.

AWS Glue can directly consume the changes captured by the CDC tool, streamlining the process of updating data warehouses and data lakes. This integration ensures that data systems are always up-to-date, supports real-time analytics, and improves overall data management efficiency.

Steps to implement a change data capture tool with Glue #

Here are the steps you need to follow to implement a change data capture tool with AWS Glue:

Compatibility with AWS Glue: Ensure the tool integrates seamlessly with Glue’s serverless architecture and ETL processes.
Scalability: Assess if the tool can handle increasing data volumes effectively.
Performance optimization: Check the tool’s ability to fine-tune performance settings based on data workload.
Data source support: Verify support for a wide range of data sources, similar to Glue’s extensive compatibility.
Cost-efficiency: Compare pricing models to ensure cost-effectiveness.

Commonly missed factors #

Infrastructure overhead: Overlooking the additional infrastructure needs of the tool.
Data consistency: Ignoring how the tool maintains data integrity across systems.

Building a business case #

Demonstrate efficiency gains: Highlight improved data processing speed and accuracy.
Cost-benefit analysis: Present long-term savings in resource management.
Enhanced data analytics capability: Show how real-time data synchronization can drive better decision-making.
Risk mitigation: Outline how the tool reduces the risks associated with manual or less efficient change data capture methods.

Guidelines for effective implementation #

Now, let’s understand the common pitfalls in implementing a change data capture tool with AWS Glue.

Incorrect technique selection: Not choosing the right change data capture method for the specific use case.
Underestimating system impact: Overlooking the load trigger-based change data capture can put on source systems.
Inadequate monitoring: Failing to properly monitor and manage the change data capture process, leading to data inconsistencies.
Overlooking glue’s capabilities: Not fully utilizing Glue’s serverless architecture and visual interface for ETL pipeline management, which can lead to inefficiencies.