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
Step 2: Create Spark Session
Step 3: Creating Data Generator class
Step 4: Method to Upsert into Hudi tables
Sample Preview of Customer Dataframe
Dataframe
领英推荐
Upserting into Hudi tables
If a customer has updated their information, we would need to apply Slowly Changing Dimension
Previous Information
New information
Sample Preview of new DF
Creating Snapshots
Marking Old record is_current to False
O/p Preview
Lets see old and new DF and lets Merge and perform UPSERT
Merging Both the records
Upsert into Hudi DIM
Reading from Hudi?
BINGO
Mission Accomplished?
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.
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6 个月Superb, Thanks Soumil.
Data Engineer @ HRS | AWS | Apache Spark | Python
1 年Hi Soumil, this was really informative post!