Selecting the Ideal Change Data Capture Strategy for Your Amazon DynamoDB Applications

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Change Data Capture (CDC) refers to the method of tracking changes within a database and relaying those changes to an event stream, which can then be accessed by various systems. Amazon DynamoDB provides a robust mechanism for capturing, processing, and responding to changes in data in near real time. Whether you’re developing event-driven applications, integrating with other services, implementing data analytics, or ensuring data consistency and compliance, CDC can serve as a crucial asset in your DynamoDB toolkit.

DynamoDB employs a streaming model for CDC, enabling applications to capture item-level changes in a table as a stream of data records. This stream allows applications to efficiently process and react to updates in the DynamoDB table. There are two primary streaming models for CDC in DynamoDB: Amazon DynamoDB Streams and Amazon Kinesis Data Streams for DynamoDB.

In this article, we will explore both DynamoDB Streams and Kinesis Data Streams for DynamoDB. We will begin with an overview of DynamoDB Streams, followed by a discussion on when and why they are beneficial for building event-driven applications and integrating with other services to gain actionable insights. Additionally, we will provide a summary of Amazon Kinesis Data Streams, highlighting scenarios where Kinesis might be the preferable choice. Finally, we will conclude with a comparative overview of both options.

DynamoDB Streams

DynamoDB Streams captures an ordered, deduplicated sequence of item-level modifications within a table and preserves this data in a log for up to 24 hours. Depending on your configuration, you can view the data items before and after they were modified. This functionality enables you to create applications that consume stream events and trigger workflows based on the contents of the event stream.

DynamoDB Streams can be particularly advantageous in scenarios such as:

• Responding to data changes through triggers, leveraging native integration with AWS Lambda. Moreover, read requests made by Lambda-based consumers of DynamoDB Streams incur no additional costs. You can also save on Lambda expenses by utilizing event filtering with DynamoDB.
• Tracking and analyzing customer interactions or monitoring application performance in near real-time, which can also support data warehousing or analytics initiatives.
• Capturing ordered sequences of events, beneficial for troubleshooting, debugging, or compliance purposes, which is critical in industries like ecommerce, financial services, and healthcare.
• Enhancing application resiliency by replicating item-level transactional data, which helps mitigate data availability challenges during regional outages or operational issues.

Example Use Case: Sending a Welcome Email Following User Registration

To illustrate the first and second applications mentioned earlier, consider a scenario where you’re building a web application that allows new users to register an account. Once they register, the system should automatically send a welcome email to the new customer and update the database with the email’s delivery status. Here’s how this could be structured using AWS:

1. A new user signs up by providing their email address.
2. The PUT/POST request creates a new item in DynamoDB, generating a corresponding DynamoDB stream record.
3. A Lambda function filters the new user event from the DynamoDB stream for processing.
4. The Lambda function sends a welcome email via Amazon Simple Email Service (Amazon SES) to the new user.
5. Amazon SES relays the email delivery status to Amazon Simple Notification Service (Amazon SNS), indicating success or failure.
6. A separate Lambda function processes the SNS message and updates the delivery status for the newly registered user in the DynamoDB table.

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Example Use Case: Global Competitive Gaming Application

In a different example that covers the third and fourth functions discussed earlier, consider a gaming application where players compete on leaderboards and real-time statistics. In this scenario, “first completion” serves as a tiebreaker for players with identical scores. Below is a high-level overview of how this application could be built using AWS:

1. Game players complete quests and update their game state through Amazon CloudFront and Amazon API Gateway.
2. A Lambda function processes the API requests from API Gateway.
3. The Lambda function creates a DynamoDB item representing the player’s game state (e.g., game realm, Region, time of completion).
4. A DynamoDB Stream record is generated for each item created, updated, or deleted, reflecting the change in state.
5. The stream record is sent to Amazon EventBridge Pipes as a source event.
6. This source event is published to an Amazon Simple Queue Service (SQS) queue in a separate Region, centralizing the game’s global state and leaderboards.
7. Another Lambda function processes SQS messages received from all gameplay Regions, creating an item in DynamoDB for the Regional game state.
8. Both the Regional and global Lambda functions filter stream records based on significant statistics, integrating with website applications or in-game displays.

This workflow exemplifies how DynamoDB Streams can facilitate a complex global competitive gaming application. Each game session is preserved Regionally while replicated globally, allowing for completion orders to be tracked across all Regions, thereby updating global leaderboards and notifying players of new records.

With DynamoDB Streams, your application can effectively capture stateful gameplay updates while maintaining strict event ordering through sequence numbers, ensuring accurate representation of completion orders both Regionally and globally. Automatic deduplication of stream records during brief connectivity issues helps to prevent incorrect ordering and inflated statistics.

In summary, selecting the right CDC strategy for your Amazon DynamoDB applications hinges on understanding the specific requirements of your use case. Whether you choose DynamoDB Streams for its simplicity and direct integration with AWS services or Kinesis Data Streams for its advanced capabilities, each option offers unique benefits to enhance your application’s performance and reliability.