Streaming ETL with Apache Flink & Debezium for CDC Pipelines
Design and build real-time data pipelines using Apache Flink and Debezium to enable low-latency Change Data Capture (CDC) processing.
Course Duration: 10 Hours
Preview Streaming ETL with Apache Flink & Debezium for CDC Pipelines course
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Streaming ETL with Apache Flink & Debezium for CDC Pipelines – Online Course
As organizations shift toward real-time data architectures, traditional batch ETL (Extract, Transform, Load) methods no longer meet the speed and agility requirements of modern analytics, decision-making, and machine learning systems. This course, Streaming ETL with Apache Flink & Debezium for CDC Pipelines, teaches you how to build highly responsive, low-latency ETL pipelines that react to changes in source databases in real time.
Change Data Capture (CDC) is a technique that captures changes in data at the source level and propagates them downstream without polling or reprocessing entire datasets. Tools like Debezium enable CDC by monitoring changes from transactional systems (MySQL, PostgreSQL, SQL Server, etc.) and emitting events. Apache Flink, on the other hand, provides a robust stream processing engine capable of transforming, enriching, filtering, and aggregating these change events with sub-second latency.
In this hands-on course, you’ll learn to integrate Flink with Debezium via Kafka to build scalable, fault-tolerant pipelines. You'll start from the basics of stream processing, CDC theory, and event-driven design, progressing toward real-world pipeline implementations.
What is Streaming ETL with Flink & Debezium?
It’s a modern data engineering approach where database changes (INSERT/UPDATE/DELETE) are streamed in real-time using Debezium, and processed through Apache Flink for real-time analytics, filtering, enrichment, routing, or transformations before landing into data lakes, warehouses, or downstream services.
How to Use This Course
- Start with the theory behind stream processing, event time, and windowing.
- Build hands-on CDC connectors using Debezium and Kafka.
- Write Flink jobs that ingest, transform, and enrich change events.
- Design fault-tolerant streaming architectures using checkpoints and stateful operators.
- Deploy pipelines to production on Flink clusters or Flink SQL gateways.
By the end of the course, you’ll be equipped to implement real-time ETL systems that are scalable, flexible, and production-ready.
Course/Topic 1 - Coming Soon
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The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.
Course Objectives Back to Top
Upon completing this course, learners will be able to:
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Understand the principles of stream processing and Change Data Capture (CDC).
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Set up Debezium connectors for MySQL/PostgreSQL databases using Kafka.
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Process and enrich change events in real time using Apache Flink.
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Use Flink DataStream and Table API for complex streaming logic.
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Handle late-arriving data using event time and watermarking.
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Implement fault-tolerant and scalable pipelines using Flink’s checkpointing and stateful operators.
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Integrate Flink with Kafka, Elasticsearch, and cloud data sinks.
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Use Flink SQL for declarative CDC processing.
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Monitor and tune streaming jobs for performance and reliability.
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Design end-to-end CDC pipelines for real-time data warehousing, analytics, and microservices.
Course Syllabus Back to Top
Course Syllabus
Module 1: Introduction to Streaming ETL & CDC
- Limitations of batch ETL
- Streaming vs. micro-batch
- What is Change Data Capture (CDC)?
Module 2: Getting Started with Debezium
- Debezium architecture and connectors
- Setting up MySQL/PostgreSQL CDC pipelines
- Kafka Connect and Avro/JSON serialization
Module 3: Apache Flink Overview
- Flink runtime, job manager, task slots
- DataStream API vs. Table API
- Setting up Flink cluster (local, Docker, cloud)
Module 4: Real-Time Event Ingestion
- Integrating Kafka topics with Flink sources
- Understanding event time, processing time, watermarks
- Handling out-of-order data
Module 5: Data Transformation and Enrichment
- Stateful stream processing
- Filtering, mapping, joining change events
- Using broadcast state for reference data
Module 6: Fault Tolerance and Checkpointing
- Enabling checkpointing and savepoints
- State backends and recovery
- Exactly-once semantics with Kafka
Module 7: CDC with Flink SQL
- Introduction to Flink SQL
- Creating CDC tables and views
- Writing streaming SQL queries for ETL logic
Module 8: Integration with Sinks
- Kafka Sink, Elasticsearch Sink, JDBC Sink
- Writing CDC events to Data Lakes (Iceberg, Delta)
- Output schema and serialization
Module 9: Project – Real-Time Inventory Tracking Pipeline
- MySQL to Kafka (via Debezium)
- Kafka to Flink transformations
- Sink to PostgreSQL + Elasticsearch dashboard
Module 10: Monitoring, Logging, and Alerting
- Using Flink’s web UI
- Prometheus/Grafana integration
- Alerting for lag, failures, and checkpoints
Module 11: Advanced Topics
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Schema evolution in CDC pipelines
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Reprocessing historical changes
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Dynamic scaling and parallelism
Certification Back to Top
On completion of this course, you will receive a Uplatz Certificate of Expertise in Streaming ETL & CDC Pipelines. This certification affirms your hands-on proficiency in setting up end-to-end Change Data Capture (CDC) pipelines using Debezium and processing those events in real time using Apache Flink.
The certificate serves as a practical proof of your capabilities in stream-first architectures, real-time transformations, fault-tolerant deployment, and low-latency data movement. It also highlights your competence in handling stateful stream processing and integrating Kafka, databases, and modern cloud data platforms.
In an industry rapidly adopting real-time analytics and operational intelligence, this credential makes you a valuable asset for roles in data engineering, platform engineering, and analytics modernization. Employers will recognize your ability to contribute to data infrastructure that powers the next generation of event-driven applications.
Career & Jobs Back to Top
Streaming data infrastructure is now a must-have for modern enterprises. Real-time decision-making, customer personalization, fraud detection, and operational monitoring all require immediate access to accurate, fresh data.
This course equips learners for career paths such as:
- Streaming Data Engineer
- Apache Flink Developer
- Kafka and CDC Pipeline Engineer
- Data Platform Engineer
- ETL Architect (Real-Time Systems)
- Debezium Integration Specialist
As companies migrate to event-driven architectures and operational analytics, there is surging demand for professionals who can build and manage scalable, low-latency data pipelines. Technologies like Flink, Kafka, and Debezium are core to this stack and mastering them positions you for highly competitive roles.
You can work in industries including finance (for fraud detection and transaction processing), e-commerce (for inventory sync and recommendations), logistics (for tracking), or telecom (for customer insights). This skillset also opens consulting opportunities for helping enterprises modernize their data platforms.
By completing this course, you gain the practical experience to not only implement Flink jobs but also understand CDC nuances, schema evolution, and real-world reliability needs—making you job-ready in one of the hottest areas of data engineering today.
Interview Questions Back to Top
1. What is Change Data Capture (CDC)?
CDC is the process of identifying and capturing changes in data (INSERT, UPDATE, DELETE) from a source system and delivering them to downstream systems in real time.
CDC is the process of identifying and capturing changes in data (INSERT, UPDATE, DELETE) from a source system and delivering them to downstream systems in real time.
2. How does Debezium work with Kafka?
Debezium monitors database logs (binlogs or WAL) and publishes change events to Kafka topics using Kafka Connect.
Debezium monitors database logs (binlogs or WAL) and publishes change events to Kafka topics using Kafka Connect.
3. What is Apache Flink, and why is it used for CDC?
Flink is a stream processing engine used to process, transform, and enrich CDC events with low latency and stateful capabilities.
Flink is a stream processing engine used to process, transform, and enrich CDC events with low latency and stateful capabilities.
4. What is the difference between DataStream API and Table API in Flink?
DataStream API is used for programmatic, low-level control, while Table API (or Flink SQL) allows declarative streaming queries.
DataStream API is used for programmatic, low-level control, while Table API (or Flink SQL) allows declarative streaming queries.
5. How does Flink achieve fault tolerance?
Using checkpoints, state backends, and recovery mechanisms, Flink provides exactly-once or at-least-once guarantees.
Using checkpoints, state backends, and recovery mechanisms, Flink provides exactly-once or at-least-once guarantees.
6. What are watermarks in Flink?
Watermarks are markers that indicate progress in event time, helping to handle late or out-of-order events.
Watermarks are markers that indicate progress in event time, helping to handle late or out-of-order events.
7. Can Flink handle schema evolution in Debezium CDC streams?
Yes, with schema registry integration and careful type mapping, Flink can process evolving schemas.
Yes, with schema registry integration and careful type mapping, Flink can process evolving schemas.
8. What is the role of Kafka in this pipeline?
Kafka acts as a durable, scalable messaging backbone between Debezium and Flink, buffering and distributing CDC events.
Kafka acts as a durable, scalable messaging backbone between Debezium and Flink, buffering and distributing CDC events.
9. What is a savepoint in Flink?
A savepoint is a manually triggered state snapshot used for upgrades, rollback, and job migration.
A savepoint is a manually triggered state snapshot used for upgrades, rollback, and job migration.
10. What are common sinks used in CDC pipelines?
PostgreSQL, Elasticsearch, Apache Iceberg, BigQuery, Kafka topics, and cloud object stores (S3, GCS) are common destinations.
PostgreSQL, Elasticsearch, Apache Iceberg, BigQuery, Kafka topics, and cloud object stores (S3, GCS) are common destinations.
Course Quiz Back to Top
FAQs
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