05 / Data Engineering / May 2026
Change Data Capture Pipeline
End-to-end CDC analytics pipeline using Debezium, Redpanda, ClickHouse, and dashboards for real-time data exploration.
01
Overview
A real-time analytics system that moves operational changes into an analytical layer without waiting for batch exports.
02
Challenge
Operational datasets are useful only when they stay fresh, but freshness can create complexity if the architecture is not explicit.
03
Outcome
The result is a portfolio-grade data engineering case study that demonstrates the full path from operational movement to analytical insight.
Project background
Why this project exists
This project started from a common data engineering problem: dashboards are expected to feel current, but many analytical systems still depend on periodic refreshes. The work explores what happens when operational change is treated as a stream instead of a scheduled copy job.
The architecture is intentionally portfolio-friendly. Debezium-style capture, streaming transport, and ClickHouse-style analytical storage make the system easy to explain from source database to dashboard. The emphasis is not only on tooling, but on the contract between systems: what changed, where it moved, and how it becomes queryable.
Build notes
How it was shaped
Used Debezium-style change capture to model inserts, updates, and deletes as event streams.
Designed Redpanda/Kafka topics as a transport layer between source systems and analytical consumers.
Structured ClickHouse tables for fast exploration, trend reads, and downstream dashboard queries.