Mastering Slowly Changing Dimension with Hudi: A Step-by-Step Guide to Efficient Data Management

Mastering Slowly Changing Dimension with Hudi: A Step-by-Step Guide to Efficient Data Management

Slowly changing dimension (SCD) is a key concept in data management that helps track changes in data over time. It is particularly useful in data warehousing and data analytics, where historical data is essential for trend analysis, forecasting, and identifying patterns. In this blog, we will discuss the advantages of using slowly changing dimensions in the context of lakehouse architecture and how it can be implemented.

Advantages of Slowly Changing Dimension in Lakehouse Architecture

Lakehouse architecture is a new paradigm that combines the best of both worlds: data warehousing and data lake. It is designed to handle large-scale data workloads and enables faster data processing and analytics. Slowly changing dimensions play a crucial role in lakehouse architecture by providing the following benefits:

1. Improved Data Integrity

Slowly changing dimensions help maintain data integrity by ensuring that the data is accurate and consistent over time. This is particularly important in lakehouse architecture, where data is stored in multiple formats and may be updated frequently. With slowly changing dimensions, you can track changes in data and ensure that the data remains consistent.

2. Efficient Data Processing

Lakehouse architecture is designed to handle large-scale data workloads, and slowly changing dimensions help in processing the data efficiently. By tracking changes in data, you can avoid processing unnecessary data and focus on the relevant data.

3. Simplified Data Analysis

Slowly changing dimensions simplify data analysis by providing a comprehensive view of the data. With SCD, you can easily track changes in data and analyze the data over time. This is particularly useful in data warehousing and data analytics, where historical data is essential for trend analysis, forecasting, and identifying patterns.

Implementation of Slowly Changing Dimension in Lakehouse Architecture

Slowly changing dimension can be implemented in lakehouse architecture using different approaches. Here are three common approaches:

1. Type 1 SCD

Type 1 SCD involves replacing old data with new data. This approach is suitable for situations where data changes are infrequent and historical data is not required. In this approach, the old data is overwritten with new data, and the changes are not tracked.

2. Type 2 SCD

Type 2 SCD involves creating a new record for the changed data, and the old record is kept as it is. This approach is suitable for situations where historical data is required. In this approach, the new record has a new primary key value and a start and end date that represent the time period during which it is valid.

3. Type 3 SCD

Type 3 SCD involves tracking only the most recent value, and the history of changes is not maintained. This approach is suitable for situations where historical data is not required, and only the most recent value is relevant.

Videos


Lets implement Slowly changing dimension with Hudi

Step 1: Define Imports

No alt text provided for this image

Step 2: Create Spark Session

No alt text provided for this image

Step 3: Creating Data Generator class

No alt text provided for this image

Step 4: Method to Upsert into Hudi tables

No alt text provided for this image

Sample Preview of Customer Dataframe

No alt text provided for this image

Dataframe

No alt text provided for this image

Upserting into Hudi tables

No alt text provided for this image

If a customer has updated their information, we would need to apply Slowly Changing Dimension

Previous Information

No alt text provided for this image

New information

No alt text provided for this image

Sample Preview of new DF

No alt text provided for this image

Creating Snapshots

No alt text provided for this image

Marking Old record is_current to False

No alt text provided for this image

O/p Preview

No alt text provided for this image

Lets see old and new DF and lets Merge and perform UPSERT

No alt text provided for this image

Merging Both the records

No alt text provided for this image


Upsert into Hudi DIM

No alt text provided for this image

Reading from Hudi?

No alt text provided for this image

BINGO

No alt text provided for this image

Mission Accomplished?

No alt text provided for this image

Complete code can be found

https://soumilshah1995.blogspot.com/2023/05/mastering-slowly-changing-dimension.html

Conclusion

Slowly changing dimensions play a crucial role in data warehousing and data analytics by providing a comprehensive view of the data. In the context of lakehouse architecture, SCDs provide numerous benefits, including improved data integrity, efficient data processing, and simplified data analysis. By implementing slowly changing dimensions in lakehouse architecture, organizations can achieve better data management and faster data processing.

Somsubhra Jana

Hiring || Python || Java || DE || ML OPs Empowering Innovation and Driving Scalable Solutions.

6 个月

Superb, Thanks Soumil.

回复
Rishabh Bhardwaj

Data Engineer @ HRS | AWS | Apache Spark | Python

1 年

Hi Soumil, this was really informative post!

回复

要查看或添加评论,请登录

社区洞察

其他会员也浏览了