How to Implement Change Data Capture with Databricks?

Updated January 10th, 2024
Change data capture for Databricks

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Combining a change data capture tool with Databricks helps organizations ensure up-to-date and accurate data analysis, leading to more informed business decisions and efficient data management.

Databricks, as a platform designed to unify data and AI, can leverage change data capture to efficiently process only the altered data, reducing system load and improving performance.

This aligns with Databricks’ objective of making AI accessible and applicable, by ensuring real-time data synchronization and accuracy, crucial for advanced analytics and decision-making.

Implementing a change data capture tool with Databricks aligns with best practices of structured planning, effective tool usage, and robust data management, further enhancing the platform’s capabilities in data processing and AI applications.


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Table of contents

  1. Why should you use a change data capture tool with Databricks?
  2. Databricks overview
  3. What is change data capture?
  4. Steps to implement a change data capture tool with Databricks
  5. Guidelines for effective implementation
  6. Change data capture for Databricks: Related reads

Why should you use a change data capture tool with Databricks?

The reasons why you should implement a change data capture tool with Databricks are numerous. Let’s take a look at some of the most important ones.

  1. Real-time data synchronization: Change data capture ensures data across different systems is updated in real-time, enhancing consistency.
  2. Efficiency in data processing: It allows processing only the changed data, reducing load and improving performance.
  3. Supports data warehousing and analytics: Change data capture is vital for keeping data warehouses updated, aiding in accurate analytics.
  4. Enables event-driven architecture: It allows systems to respond immediately to data changes, supporting responsive and agile business operations.

Databricks overview

Databricks is a data intelligence platform integrating AI, ETL, data ingestion, business intelligence, and governance, enabling users to own and innovate with their data and AI.

It streamlines complex analytics across big data and machine learning, empowering teams to collaborate and drive insightful decisions from their data.


What is change data capture?

Change data capture is a data integration pattern that tracks when and what changes occur in data, and then alerts other systems and services that need to respond to these changes.

It helps maintain consistency and functionality across all systems that rely on data.

Combining change data capture with Databricks offers several benefits:

  • Real-time analytics: Databricks’ AI and data unification enhance change data capture’s ability to provide up-to-date data, crucial for timely analytics.
  • Efficient data management: By processing only changed data, Databricks and change data capture together reduce system workload, improving overall efficiency.
  • Enhanced decision making: The combination supports informed decisions through consistent and current data analysis.
  • Scalability: Databricks’ scalability complements change data capture’s adaptability to data volume and velocity, ensuring the system grows with organizational needs.

Steps to implement a change data capture tool with Databricks

Here are the steps to implement a change data capture tool with Databricks. Let’s dive in!

  • Compatibility with Databricks: Ensure the change data capture tool integrates smoothly with Databricks’ data architecture and AI capabilities.
  • Scalability and performance: The tool should handle the expected data volume and velocity without performance degradation.
  • Data quality assurance: Look for features that validate and cleanse data during capture.
  • Security and compliance: The tool must adhere to security standards and data governance policies.
  • Ease of configuration and use: Prioritize user-friendly interfaces and simple configuration processes.
  • Monitoring and support: Choose a tool with robust monitoring capabilities and reliable support.
  • Cost-effectiveness: Assess the total cost of ownership, including licensing, maintenance, and scalability costs.

Common oversights


  • Underestimating data volume: Not considering the future growth of data can lead to scalability issues.
  • Neglecting advanced features: Overlooking advanced features like real-time analytics can limit the tool’s effectiveness.

Making a business case


  • Highlight efficiency gains: Emphasize how the right change data capture tool can enhance efficiency in data processing and data analytics.
  • Demonstrate ROI: Show potential savings in time and resources, and how it leads to better decision-making capabilities.
  • Align with business goals: Link the tool’s benefits to specific organizational objectives and outcomes.

Guidelines for effective implementation

Here are some common mistakes when implementing a change data capture tool with Databricks:

  • Underestimating data volume: Overlooking the scale of data changes can overwhelm the system.
  • Neglecting data quality: Failing to ensure data accuracy leads to unreliable analytics.
  • Improper configuration: Incorrect setup of change data capture tools disrupts data synchronization.
  • Inadequate security measures: Overlooking security and compliance needs risks data breaches.
  • Insufficient monitoring: Lack of ongoing surveillance can cause unnoticed system failures.
  • Ignoring Databricks integration: Not fully leveraging Databricks’ features can limit the potential of change data capture.

Delta Lake’s Change Data Feed for change data capture


Delta Lake’s Change Data Feed (CDF) is a feature within Delta Lake, integrated into the Databricks platform, that facilitates Change Data Capture (CDC) by tracking changes such as inserts, updates, and deletes in a Delta table.

Implementing CDC with Databricks using Delta Lake’s CDF involves enabling the CDF on Delta tables, which automatically records data changes. Users can then query these changes through Delta Lake’s APIs, allowing for efficient processing and handling of change data for various applications like real-time analytics and data synchronization.

This setup benefits from Databricks’ scalable processing and Delta Lake’s data versioning and change tracking capabilities, making it an efficient solution for managing data changes in large-scale environments.



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