Introduction: Why Change Data Capture (CDC) Matters in Modern Data Engineering

Change Data Capture (CDC) has become one of the most essential data engineering techniques for modern analytics, real-time dashboards, machine learning pipelines, and data warehousing. Businesses today generate enormous volumes of data every second. Instead of reloading entire datasets, CDC allows data engineers to capture only the changes—inserts, updates, and deletes—significantly reducing processing time and cost.

In this blog post, we dive deep into what CDC is, how it works, where it fits in ETL and ELT pipelines, and what tools and best practices are used across cloud environments like AWS, Azure, and GCP. If you’re exploring real-time data pipelines or preparing for a data engineering role, this guide is for you.

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What Is Change Data Capture (CDC)?

Change Data Capture (CDC) is a method for identifying and capturing changes made in a source system so that downstream systems—like data lakes, warehouses, and analytics dashboards—stay updated without reprocessing the entire dataset.

CDC captures three main types of data modifications:

• INSERT
• UPDATE
• DELETE

This helps maintain an efficient, incremental, and near real-time synchronization between systems.

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Why CDC Is Critical in Modern ETL Pipelines

As companies move from batch to real-time processing, CDC becomes essential for:

• Streaming analytics
• Event-driven architecture
• Real-time dashboards
• Machine learning feature stores
• Data replication across regions or systems
• Cloud migration and modernization

CDC dramatically improves performance by eliminating the need for full data reloads.

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How CDC Works: Core Concepts Explained

1. Change Identification

The system identifies changes in the source database using:

• Timestamps
• Log files (WAL, redo logs)
• Table comparison
• Version columns

2. Change Extraction

Captured changes are extracted and formatted as change events.

3. Change Delivery

Changes are delivered to the target system or transformation engine.

4. Apply Changes to Target

The final step updates your:

• Data lake
• Lakehouse
• Data warehouse
• Real-time analytics engine

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Types of CDC

Different CDC techniques are used across environments. Each has pros and cons depending on scale, performance, and database capabilities.

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1. Timestamp CDC

Rows that changed after a certain timestamp are extracted.

Pros

• Simple
• Fast for small datasets

Cons

• Missing updates due to clock drift
• Not truly real-time

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2. Trigger-Based CDC

Database triggers record changes into audit tables.

Pros

• Accurate
• Works on older systems

Cons

• Adds overhead
• Requires DML trigger management

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3. Log-Based CDC (Best Method)

Reads changes directly from transaction logs such as:

• MySQL binlog
• SQL Server transaction log
• PostgreSQL WAL
• Oracle redo logs

Pros

• Highly efficient
• Real-time
• No table locks

Cons

• Requires database access permissions

This is the most widely used CDC method today.

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4. Table Diff (Snapshot Comparison)

Full table comparison detects differences.

Not recommended for large datasets.

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Popular CDC Tools Used in the Industry

1. Debezium

Open-source, log-based CDC for:

• MySQL
• PostgreSQL
• MongoDB
• SQL Server

Often paired with Kafka.

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2. AWS DMS

AWS Database Migration Service supports real-time CDC into:

• S3
• Redshift
• DynamoDB
• Kinesis

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3. Azure Data Factory CDC

Supports:

• SQL Server
• Oracle
• Cosmos DB

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4. Google Cloud Datastream

Serverless CDC for:

• MySQL
• Oracle
• PostgreSQL

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5. Fivetran / Hevo

Fully managed CDC connectors.

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CDC vs Full Load – Why Incremental Wins

A full load retrieves 100% of the data every time, which is slow and expensive.

CDC retrieves only the changed rows.

Feature	Full Load	CDC
Performance	Slow	Fast
Cost	High	Lower
Real-Time	❌	✔️
Use Cases	One-time loads	Continuous sync

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CDC in Data Lakes and Lakehouses

Modern ETL frameworks such as Databricks Delta Lake and Apache Hudi support CDC natively.

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CDC Into Delta Lake

Delta Lake supports:

• MERGE operations
• Upserts
• SCD Type 2
• Audit history

Example MERGE:

MERGE INTO sales_silver AS target
USING sales_raw_cdc AS source
ON target.id = source.id
WHEN MATCHED AND source.op = 'UPDATE' THEN UPDATE SET *
WHEN MATCHED AND source.op = 'DELETE' THEN DELETE
WHEN NOT MATCHED THEN INSERT *

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CDC and SCD Type 2 (Slowly Changing Dimensions)

CDC is the backbone of SCD2 pipelines.
SCD2 ensures historical tracking of attributes, such as:

• Customer address change
• Product attribute update
• Employee role changes

CDC identifies the change; SCD2 manages versioning.

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CDC Architecture in Real Projects

Below is a typical real-time CDC architecture:

Source DB → CDC Capture → Kafka/Streams → ETL/Transform → Lakehouse/Warehouse → Analytics

Or cloud-native:

AWS Example

Oracle → AWS DMS → S3 → Glue/EMR → Redshift

Azure Example

SQL Server → ADF CDC → ADLS → Databricks → Synapse

GCP Example

MySQL → Datastream → GCS → Dataflow → BigQuery

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Best Practices for Implementing CDC

✔ Choose log-based CDC for high performance

✔ Always implement idempotent transformations

✔ Maintain audit logs

✔ Use checkpointing for fault tolerance

✔ Partition CDC tables by event time

✔ Avoid applying CDC directly to gold tables

✔ Test failure recovery and backfills

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Common Challenges in CDC

Even though CDC is powerful, it comes with challenges:

• Schema drift
• Handling deletes
• Late arriving records
• Network latency
• Transaction ordering issues
• High-velocity workloads

Modern tools like Debezium and Datastream have built-in solutions.

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Conclusion: CDC Is the Future of Modern ETL

As businesses push toward real-time data platforms and event-driven architectures, CDC becomes essential. It makes ETL pipelines faster, cheaper, scalable, and more intelligent. Whether you’re building cloud data pipelines, migrating databases, or implementing SCD2, mastering CDC is a must-have skill for every data engineer.

This module gives you the complete understanding needed to work with Change Data Capture using modern cloud and open-source tools. If you’re preparing for interviews or working on enterprise-grade projects, CDC will be one of the most valuable components in your ETL toolkit.