Build a Real-Time Analytics Engine with Apache Flink, Kafka, and ClickHouse
Are you still relying on nightly batch jobs? In the world of high-scale systems, the ability to process and react to data the exact moment it is generated is a competitive necessity. In this video, we dive deep into Apache Flink, the open-source stream and batch processing framework that treats everything as a continuous stream by default. Using a URL Shortener application as our practical case study, we will show you how to transform raw events into actionable insights. We will break down the complete modern decoupled streaming pipeline, taking data from an Event Producer, buffering it in Apache Kafka, processing it in Flink, and storing it in ClickHouse for ultra-fast, real-time aggregated queries. Whether you want to know when to use Flink SQL over the Java DataStream API or how to handle backpressure and node crashes, this video has you covered!
📌 What You Will Learn in This Video:
0:00 - Introduction: Why real-time processing beats micro-batching and batch jobs
1:15 - Core Concepts: Event-Time Processing, Stateful Computations, and Exactly-Once Semantics.
3:30 - Pipeline Architecture: Integrating Flink with Apache Kafka and ClickHouse.
5:00 - Flink Runtime: Understanding the JobManager (The Orchestrator) vs. TaskManager (The Worker).
7:20 - Flink SQL vs. Java DataStream API: When to use declarative ETL versus imperative control.
10:45 - Kubernetes Deployment: Best practices for external configuration using K8s ConfigMaps.
12:30 - Real-time Aggregations: Using ClickHouse Materialized Views for high-speed dashboards.
14:15 - Advanced Patterns: Detecting bot traffic with Keyed State, analyzing trends with Sliding Windows, and stream branching with Side Outputs.
17:00 - Operational Resilience & Fault Tolerance: The difference between Checkpoints (Auto-save) and Savepoints (Manual Backup).
19:30 - Flink Alternatives: How Flink compares to Apache Spark Streaming, Kafka Streams, and Cloud Dataflow.
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