Data Science
Advanced
Real-Time Streaming Analytics with Apache Flink
Process stateful real-time data streams with low latency using Apache Flink and Kafka.
Course Overview
Master event-time temporal joins, sliding window aggregations, state checkpointing, and exactly-once processing guarantees in distributed streaming pipelines.
What You Will Learn
Architect real-time event processing pipelines with Apache Flink and Kafka
Master event-time temporal windows: tumbling, sliding, and session windows
Manage stateful operators, RocksDB state backends, and savepoint snapshots
Implement event stream joins, interval joins, and temporal table enrichment
Ensure exactly-once processing guarantees across distributed streaming clusters
Tools & Technologies Used
Apache Flink
PyFlink
Apache Kafka
Docker
Python 3.11
Structured Curriculum
2 Modules · 4 Lessons · 260 Minutes Total
Module 1
Module 1: Flink Streaming Core & Windowing
2 lessons
Module 1: Flink Streaming Core & Windowing
Understand stream execution graphs, watermarks, and windowing semantics.
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Event Time vs. Processing Time & Watermarks
Handle out-of-order event arrivals and late data using watermark generators.
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Tumbling, Sliding & Session Window Aggregations
Implement real-time window aggregations over high-throughput event streams.
Module 2
Module 2: State Management & Stream Joins
2 lessons
Module 2: State Management & Stream Joins
Manage operator state, perform stream-stream joins, and configure savepoints.
-
Stateful Stream Processing & RocksDB Backends
Configure keyed state, state TTL policies, and persistent RocksDB checkpoints.
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Temporal Stream Joins & Kafka Integration
Join real-time clickstreams with dynamic user lookup tables stored in Kafka topics.
Practical Project & Capstone Outcome
🚀 Capstone Project
Real-Time Financial Transaction Fraud Alerting Engine
Build a PyFlink streaming job connected to Kafka that processes credit card transactions, evaluates velocity sliding windows, and flags fraud anomalies in under 50ms.
Prerequisites
- Understanding of event-driven concepts
- Proficiency in Python or Java
Intended Audience
- Data Engineers building high-volume streaming platforms
- Backend Systems Architects designing low-latency event processing services
Instructor Information
V
Viktor Hansen
Course Author & Industry Expert
Viktor Hansen is a Distributed Systems Architect with 10+ years of experience engineering real-time streaming platforms for telecom and fintech leaders.
Frequently Asked Questions
Is PyFlink (Python API) used in this course?
Yes! All lessons and practical exercises use the PyFlink Python API.