In today’s data-driven world, organizations increasingly rely on real-time or near-real-time data to make informed decisions. Change Data Capture (CDC) is a vital mechanism that helps achieve this by identifying, capturing, and applying changes made to a data source in real time or at scheduled intervals. This article will explore the core concepts of CDC, the challenges it presents, and how its modern counterpart, Change Data Feed (CDF), addresses these challenges with examples.
What is Change Data Capture (CDC)?
Change Data Capture (CDC) is the process of detecting changes made to source data—whether it’s an addition, update, or deletion—and propagating those changes to a target system, such as a data warehouse or another database.
CDC can be applied in various situations, such as:
Data Warehousing: Synchronizing OLTP (Online Transactional Processing) systems with OLAP (Online Analytical Processing) systems for real-time analytics.
Replication: Maintaining data consistency across multiple systems.
Audit Trails: Tracking changes made to records for compliance and auditing purposes.
The collection of updated records for a specific table during a refresh period is called a change set. In a change set, records that share the same primary key form a recordset, representing all changes to a single record during that time.
Example:
Imagine you have a customer database having a table like below. The following operations which are done further are recorded and propagated as shown in the below tables.
Insert:
- Insert record for David.
After Insert:
Update:
- Update Alice’s city from New York to Boston.
Delete:
- Delete Bob’s record.
After Delete:
Comparison Output (With CDF):
In the _change_type column:
update_preimage captures the state of the row before the update (e.g., Alice in New York).
update_postimage shows the state of the row after the update (e.g., Alice in Boston).
delete records rows that were deleted (e.g., Bob’s record).
insert captures newly added rows (e.g., David’s record).
Why is Change Data Capture Important?
CDC offers several benefits for organizations:
1. Real-Time Data Processing: CDC enables real-time data integration, ensuring that downstream systems always have the most up-to-date information.
2. Data Consistency: By continuously capturing changes, CDC keeps multiple databases and systems synchronized.
3. Improved Performance: Since CDC processes only changes instead of bulk loading, it significantly reduces system resource usage and speeds up data processing.
Challenges with Traditional CDC
Although CDC plays a critical role in data synchronization, traditional implementations present several challenges:
1. Complexity in Tracking Changes: Keeping track of changes, especially at the row level, between multiple versions of a record can be challenging. Most implementations operate at the file level, capturing unchanged rows alongside modified ones.
2. Operational Inefficiency: Traditional CDC solutions often process entire tables or files, even if only a few rows have changed. This leads to inefficiencies in data pipelines, especially in large-scale data systems.
3. Historical Data Management: Capturing historical data is difficult in systems that do not track versioned records, leading to the potential loss of critical audit information.
Introducing Change Data Feed (CDF): Addressing CDC Challenges
To overcome the limitations of traditional CDC, a Change Data Feed (CDF) was developed. CDF is a more efficient, granular approach that captures changes at the row level in Delta Lake tables, focusing only on modified records.
Key Features of CDF
1. Granular Change Tracking: CDF tracks changes at the row level, significantly reducing overhead compared to file-level tracking.
2. Efficiency in Data Processing: CDF allows users to focus on only the rows that have changed between versions of a dataset, optimizing downstream operations such as Merge, Update, and Delete.
3. Forward-Looking Approach: CDF starts capturing changes from the moment it is enabled on a Delta Lake table. It does not retrospectively capture changes from before its activation.
How to Enable Change Data Feed (CDF)
CDF can be enabled on a Delta Lake table either during the table’s creation or on an existing table by altering its settings. It can also be activated at the cluster level, applying to all tables created within that cluster.
Example: Enabling CDF for a Bronze Table
In a typical data pipeline, you may have a Bronze Table where raw data is ingested. You can enable CDF during its creation as follows:
Alternatively, if the table already exists, you can enable CDF using an `ALTER` statement:
Once enabled, CDF will begin capturing all changes, providing an efficient mechanism for tracking updates at the row level without processing unchanged records.
Benefits of CDF for Data Engineering
CDF significantly enhances the way data engineers handle CDC by providing a simple, efficient, and scalable solution. Some of its benefits include:
Reduced Processing Overhead: CDF minimizes unnecessary processing by focusing only on changes, which is especially important for large datasets.
Improved Performance: Downstream operations such as merge and update are significantly faster when using CDF, as only modified rows are processed.
Better Version Control: CDF enables more precise control over historical data, ensuring that changes are tracked at a granular level.
Conclusion
Change Data Capture (CDC) has been a critical tool in managing data synchronization and real-time data integration. However, traditional CDC approaches often come with challenges related to complexity, inefficiency, and data management. Change Data Feed (CDF) addresses these issues by providing a streamlined, row-level solution that captures only modified records, improving both the efficiency and simplicity of data pipelines. Whether you are building real-time applications, data warehouses, or maintaining audit trails, implementing CDF can drastically reduce your data processing overhead and increase the overall performance of your systems.
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Author: Kattunga Anjali Punya Sri