CDC for Delta Live Tables: A game-changer for real-time data pipelines

Change Data Capture (CDC) is a process of tracking changes to data in a source table and propagating those changes in a target table. CDC can help you get the most out of your data by enabling you to track changes to data in real time and propagate those changes to other systems.

But implementing CDC can be complex, time-consuming, and expensive. That’s where Delta Live Tables comes in.

Delta Live Tables is a powerful tool that can help you implement CDC. It’s a declarative data pipeline management system that provides a simple set of SQL and Python APIs that you can use to define your data pipelines. Delta Live Tables takes care of the rest, including automatically handling data ingestion, transformation, and writeback.

Here are a few examples of how Delta Live Tables can be used for CDC:

• Real-time data analytics: Use Delta Live Tables to track changes to customer data in real time and update customer dashboards accordingly. This allows for a real-time understanding of how customers are behaving so that relevant changes to a business can be made.
• Data integration: Use Delta Live Tables to integrate data from a CRM system into a data warehouse. This allows for a single, unified view of customer data so that decisions can be made more easily.
• Data auditing: Use Delta Live Tables to track changes to financial data to ensure that all changes are properly accounted for. This allows for compliance with financial regulations and improvement of the accuracy of financial reporting.

If you’re looking for a way to implement CDC in your own data pipelines, I highly recommend Delta Live Tables.

How to implement CDC with Delta Live Tables

To implement CDC with Delta Live Tables, you’ll first need to create a streaming table for the source table. You can do this using either SQL or Python.

import dlt
import csv
from pyspark.sql import *
from pyspark.sql.functions import *


@dlt.table(name="customer_cdc")
def customer_cdc():
    return spark.read.option("header", "true").csv("/customer.csv")

Once you have created the streaming table, you need to define the change feed. The change feed is a logical representation of the changes to the source table. You can define the change feed using the APPLY_CHANGES statement in SQL or the apply_changes() function in Python.

dlt.create_target_table(name="customers")

dlt.apply_changes(
  target = "customers", 
  source = "customer_cdc",
  keys = ["customer_id"], 
  sequence_by = col("operation_date"),
  ignore_null_updates = False,
  apply_as_deletes = expr("operation = 'DELETE'"), 
  except_column_list = ["operation", "operation_date"],
  stored_as_scd_type = "2")

Finally, you need to start the streaming table to propagate the changes to the target table.

Example Implementation in Python

Here is a simple example of how to implement CDC using Delta Live Tables to track changes to a customer table and propagate those changes to an order table using Python.

import dlt
import csv
from pyspark.sql import *
from pyspark.sql.functions import *


@dlt.table(name="customer_cdc")
def customer_cdc():
    return spark.read.option("header", "true").csv("/customer.csv")
  
dlt.create_target_table(name="customers")

dlt.apply_changes(
  target = "customers", 
  source = "customer_cdc",
  keys = ["customer_id"], 
  sequence_by = col("operation_date"),
  ignore_null_updates = False,
  apply_as_deletes = expr("operation = 'DELETE'"), 
  except_column_list = ["operation", "operation_date"],
  stored_as_scd_type = "2")

This code will create a streaming table called ‘customers’. The ‘customers’ table will be populated with data from the ‘customer_cdc’ streaming table.

Once you have run the above code, the ‘customers’ table will start streaming data. You can then query the ‘customers’ table to get the latest changes to the order status for each customer.

Benefits of using Delta Live Tables for CDC

• Simplified implementation: Delta Live Tables makes it easy to implement CDC with a simple set of SQL and Python APIs.
• Reliability: Delta Live Tables is a reliable and scalable platform for implementing CDC.
• Performance: Delta Live Tables is optimized for performance, so you can be sure that your CDC pipelines will be fast and efficient.
• Support for SCDs: Delta Live Tables supports both SCD type 1 and SCD type 2 dimensions.

Delta Live Tables is a powerful tool that can be used to implement CDC for a variety of use cases. If you are looking for a reliable, scalable, and performant way to implement CDC, Delta Live Tables is a great option.