Streamlining Data Updates with Change Data Capture (CDC) using Delta Lake in PySpark

In a data-driven world, keeping our data up-to-date and synchronized across different systems is crucial for business operations and decision-making. One common challenge many organizations face is efficiently capturing and processing changes to their data, especially when dealing with large volumes of data and complex workflows. In this blog post, I'll walk you through how we can tackle this challenge using Change Data Capture (CDC) techniques with Delta Lake in PySpark.

Understanding the Scenario

Let's consider a scenario where we have a master dataset stored in Delta Lake, representing the latest state of our data. Alongside, we have a delta file containing new records or updates to existing records. Our goal is to efficiently update the master dataset with the changes captured in the delta file using CDC techniques.

Architecture Overview

Here's a high-level overview of our CDC process:

           +-------------------------+
           |                         |
           |       Master File       |
           |    (Delta Lake Table)   |
           |                         |
           +-------------------------+
                       |
          +------------+-------------+
          |                          |
          |         CDC Process      |
          |                          |
          +------------+-------------+
                       |
          +------------+-------------+
          |            |             |
   +------+---+   +----+---+    +----+---+
   | Insertion |   | Update  |   | Deletion |
   |  Records  |   | Records |   | Records  |
   +-----------+   +---------+   +----------+
          |            |             |
          +------------+-------------+
                       |
                       |
           +-----------+-----------+
           |                       |
           |      Delta File       |
           |    (New Records/      |
           |      Updates)         |
           |                       |
           +-----------------------+
        

Implementation Steps

• Load Master and Delta Files: We start by loading both the master file and the delta file into PySpark DataFrames.

master_df = spark.read.format("delta").load("path/to/master_file")
delta_df = spark.read.format("delta").load("path/to/delta_file")        

• Identify New Records and Updates: Next, we identify new records and updates in the delta file based on the primary key and updated timestamp.

#new records
new_records_df = delta_df.join(master_df, delta_df.primary_key == master_df.primary_key, "left_anti")

#Updates
update_records_df = delta_df.join(master_df, delta_df.primary_key == master_df.primary_key, "inner") \
    .filter(delta_df.updated_timestamp > master_df.updated_timestamp)
        

• Upsert Changes into Master File: We perform upsert operations to insert new records and update existing records in the master file.

master_df = master_df.union(new_records_df)
master_df = master_df.alias("m").join(update_records_df.alias("u"), "primary_key", "left_outer") \
    .selectExpr("coalesce(u.primary_key, m.primary_key) as primary_key", ...)        

• Final Steps: Lastly, we write the updated master file back to Delta Lake and perform any additional actions, such as vacuuming the table.Note: VACUUM command is used to clean up Delta Lake transaction logs and remove files that are no longer needed. RETAIN 168 HOURS (7 days): This clause specifies how long to retain the log files before they are eligible for cleanup.

master_df.write.format("delta").mode("overwrite").save("path/to/master_file")
spark.sql("VACUUM delta.`path/to/master_file` RETAIN 168 HOURS")        

Conclusion

By leveraging Change Data Capture (CDC) techniques with Delta Lake in PySpark, we can efficiently capture and process changes to our data, ensuring that our master dataset remains up-to-date and synchronized with the latest changes. This approach not only improves data reliability but also enhances the efficiency of our data workflows, enabling better decision-making and insights for our organization.

In summary, embracing CDC methodologies with Delta Lake empowers us to harness the full potential of our data, driving business growth and innovation.

Have you encountered similar data synchronization challenges in your projects?

#Databricks #DeltaLake