Implementing slowly changing dimensions in streaming analytics platforms.

22 Sep 2023

Managing changes to data over time is a common challenge when working with streaming analytics platforms. Slowly Changing Dimensions (SCDs) refer to the process of tracking and managing changes to data in real-time, allowing businesses to keep a historical record of changes to their data.

In this blog post, we will explore how to implement Slowly Changing Dimensions in streaming analytics platforms, such as Apache Flink and Apache Kafka, to effectively manage data changes and maintain a comprehensive view of data history.

Understanding Slowly Changing Dimensions

SCDs are commonly used in data warehousing to manage changes to data over time. There are different types of SCDs, namely Type 1, Type 2, and Type 3, each with its own approach to handling data changes.

Type 1 SCD: This method overwrites the existing values with the new values when changes occur. It doesn’t maintain a history of changes.
Type 2 SCD: This method keeps a history of changes by creating new records for each change. It adds a timestamp or versioning to differentiate between different versions of the data.
Type 3 SCD: This method stores both the old and new versions of the data within the same record, using separate columns to track changes.

Implementing Slowly Changing Dimensions in Streaming Analytics Platforms

To implement Slowly Changing Dimensions in streaming analytics platforms, we need to consider the following steps:

Data Source: Identify the source of the streaming data that requires SCD processing. It can be a real-time data stream from various sources, such as IoT devices, social media platforms, or transactional databases.
Data Transformation: Apply the appropriate SCD method (Type 1, Type 2, or Type 3) to transform the incoming data stream. This step involves comparing the incoming data with the existing data and determining whether to update the existing record or create a new one.
Data Storage: Choose a suitable storage mechanism for storing the transformed data. This could be a relational database, a data lake, or a distributed file system based on the requirements of your streaming analytics platform.
Data Retrieval: When accessing the data, consider the SCD method implemented and retrieve the desired version of the data based on the timestamp, versioning, or other indicators set during the transformation phase.

Example Implementation using Apache Flink

Let’s take an example of implementing Type 2 SCD using Apache Flink, a popular streaming analytics platform:

DataStream<RawData> input = ... // Input data stream

DataStream<ProcessedData> processedData = input
  .keyBy(RawData::getId)
  .process(new Type2SCDProcessFunction())
  .name("Type 2 SCD");

processedData.addSink(...); // Store processed data

In this example, we utilize Apache Flink’s process function to handle the Type 2 SCD logic. The process function compares the incoming data with the existing data, creates new records if necessary, and outputs the processed data stream.

Conclusion

Implementing Slowly Changing Dimensions in streaming analytics platforms is essential for maintaining an accurate and historical view of data changes. By understanding the different types of SCDs and following the necessary steps, we can effectively handle data changes in real-time.

Streaming analytics platforms like Apache Flink and Apache Kafka provide powerful tools and features to implement SCDs and ensure data consistency and integrity. Incorporating SCDs in your streaming analytics workflow can greatly enhance your data analysis and decision-making processes.

#StreamingAnalytics #SlowlyChangingDimensions