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🔄 Change Data Capture (CDC) Explained | Real-Time Data Sync | Debezium + Kafka + Airbyte + Fivetran

In this video, we deep dive into Change Data Capture (CDC) — a powerful technique for keeping your data systems in sync with real-time changes.

00:00 – Intro & Motivation

Real-world need for capturing source changes in real-time

Why batch jobs aren't sufficient anymore

01:43 – Problems with Traditional Batch Processing

Latency issues

Full data scans

Resource-intensive and inefficient

03:42 – What is Change Data Capture (CDC)?

Definition and significance

Benefits in modern data pipelines

05:26 – Types of CDC: Push vs Pull

Push: Source pushes updates

Pull: Target polls for changes

Use case comparisons

07:12 – Stream CDC Architecture (Debezium + Kafka)

How Debezium reads from DB logs

Kafka as a transport layer

Real-time flow explanation

11:35 – Batch CDC Tools: Airbyte & Fivetran

Overview of how they work

When batch CDC is still useful

Simpler setup for many use cases

14:30 – Choosing the Right CDC Strategy

Stream vs batch comparison

Tradeoffs in latency, complexity, cost

15:45 – Conclusion

Recap and closing thoughts

Best practices in applying CDC


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