Use change data capture (CDC)

Completed

Implementing Change Data Capture (CDC) in Delta Live Tables (DLT) with Azure Databricks involves capturing and processing changes made to the data in real-time. This approach enables efficient data warehousing and analytics by ensuring that the data remains up-to-date. The following steps outline how to implement CDC in Delta Live Tables with Azure Databricks:

1. Setting Up the Environment: To start, ensure you have an Azure Databricks workspace. Within this workspace, create a cluster configured to use Delta Lake. Delta Live Tables should be enabled within your Databricks environment. This requires permissions and configurations, like enabling Delta Live Tables and installing necessary libraries and dependencies.

   

2. Creating the Source and Target Tables: Define your source table, which is the table where changes (inserts, updates, deletes) will be monitored. The target table is where the changes are applied and stored. This table should be defined as a Delta table to use the Delta Lake functionalities. Ensure that your source table supports CDC by having change tracking mechanisms, such as timestamps or version numbers.

• Define source table with change tracking
• Define target Delta table

from delta.tables import DeltaTable
from pyspark.sql.functions import col

# Define the source and target paths
source_path = "/mnt/source_data"
target_path = "/mnt/target_data"
# Read the source data
source_df = spark.read.format("delta").load(source_path)

# Define the DeltaTable for the target
target_table = DeltaTable.forPath(spark, target_path)

3. Configuring Delta Live Tables Pipelines: Use Delta Live Tables pipelines to define the data flow and transformation logic. This includes specifying the source table, the transformations required to capture and process the changes, and the target table where the processed data are stored. Delta Live Tables allow you to write the pipeline using SQL or Python, using the power of Delta Lake's ACID transactions and schema enforcement.

• Define data flow and transformation logic
• Use SQL or Python for pipeline definition
• Use Delta Lake's ACID transactions and schema enforcement

# Merge logic for CDC
(target_table.alias("target")
 .merge(
     source_df.alias("source"),
     "target.id = source.id"
 )
 .whenMatchedUpdateAll()
 .whenNotMatchedInsertAll()
 .execute())

4. Implementing Change Data Capture Logic: Implement the CDC logic within your Delta Live Tables pipeline. This process involves detecting changes in the source table. This logic looks for new rows or updates to existing rows, and applying these changes to the target table. Delta Live Tables provide functions to easily merge changes into the target table, ensuring that it remains synchronized with the source table.

• Detect changes in the source table
• Apply changes to the target table
• Use Delta Live Tables functions for merging changes

# Define the Delta Live Tables pipeline
@dlt.table
def source():
    return spark.read.format("delta").load(source_path)

@dlt.table
def target():
    source_data = dlt.read("source")
    target_data = dlt.read("target")
    
    # Merge source data into the target
    target_table.alias("target").merge(
        source_data.alias("source"),
        "target.id = source.id"
    ).whenMatchedUpdateAll().whenNotMatchedInsertAll().execute()
    return target_data

5. Monitoring and Managing the Pipeline: Once the CDC pipeline is implemented, it’s crucial to monitor its performance and manage any issues that arise. Azure Databricks provides tools for monitoring the execution of Delta Live Tables pipelines, such as logging and alerting mechanisms. Regularly review the pipeline's performance, optimize transformations, and ensure that the data remains consistent and up-to-date.

• Monitor pipeline performance
• Utilize logging and alerting mechanisms
• Optimize transformations and ensure data consistency

Implementing Change Data Capture (CDC) in Delta Live Tables with Azure Databricks keeps your data accurate and up-to-date by capturing and processing real-time changes efficiently. This implementation uses Delta Lake’s ACID transactions and schema enforcement, along with Delta Live Tables’ data flow and transformation capabilities. By setting up the environment, defining tables, configuring CDC pipelines, and monitoring the system, organizations can achieve reliable and scalable data integration that supports business needs and improves decision-making.