Stream Processing: Hands-on with Apache Flink
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Stream Processing: Hands-on with Apache Flink

About the Book

This is a complete hands-on book about Apache Flink.

The book follows real-life use cases and you will learn how to create end-to-end stream processing pipelines.

We will be using Redpanda and Apache Kafka - along with other technologies - so an understanding of Apache Kafka and Redpanda concepts like topics/partitions and producers/consumers is nice to have.

The book is designed to teach you the theory and the practicals as fast as possible. The reader should be able to get from zero to production-ready applications fast with enough practice on the concepts introduced in the book, along with having enough knowledge to debug and troubleshoot when things go wrong.

Hope you will enjoy it and use it as a guide in your journey in the land of streams.

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  • Categories

    • Distributed Systems
    • Event-Driven Architecture
    • Software Engineering
    • Data Science
    • Java
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About the Author

Giannis Polyzos
Giannis Polyzos

Giannis is focusing on stream processing enablement and architecture - experienced in event streaming and stream processing systems.

He helps companies modernize their data infrastructure by leveraging streaming technologies and enables them to build fast and scalable data pipelines, streaming data flows and deliver real-time analytics.

He is passionate about event streaming systems, stateful stream processing and cloud-native data architectures.

Current tech stack focuses mainly @ Apache Flink, Apache Paimon, Redpanda, Apache Pulsar and Kubernetes

Table of Contents

    • Introduction
      • In the land of streams
      • The Streaming Layer: Redpanda
      • Flink’s Runtime
      • Summary
    • Foundations of Flink SQL
      • Streaming SQL Semantics
      • Flink SQL Logical Components
      • Running SQL Queries
      • Operators
      • The TableEnvironment
      • Summary
    • Computations over Unbounded Data Streams
      • The Notion of Time
      • Time Windows
      • What is a Watermark?
      • How do watermarks work?
      • Watermark Generation
      • Watermark Propagation
      • Idle Sources
      • Summary
    • Dynamic Table Joins
      • Introduction
      • Regular Joins
      • Interval Joins
      • Temporal Joins
      • Lookup Joins
      • Summary
    • User Defined Functions
      • Scalar Functions
      • Table Functions
      • Aggregate & Table Aggregate Functions
      • External Service Lookup UDF
      • Summary
    • The Datastream API
      • Sources
      • Datastream Operators
      • Merging Multiple Streams
      • Event Buffering & Enrichment
      • Handling Late Arriving Data
      • Summary
    • Fault Tolerance
      • Why the need for checkpoints?
      • Failure in Practise
      • Flink’s Checkpointing Algorithm
      • Aligned and Unaligned Checkpoints
      • Checkpoints vs. Savepoints
      • Summary
    • State Backends
      • State Backends
      • Using RocksDB
      • Inspecting RocksDB
      • Tuning and Troubleshooting
      • Summary
    • Monitoring and Troubleshooting
      • Metrics System
      • Prometheus and Grafana Setup
      • Setting up Flink Dashboards
      • Troubleshooting tips
      • Summary

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