Apache Flink

Master Apache Flink, the high-performance big data processing framework, and unlock its capabilities for real-time and batch data pipelines.

Who Should Attend

This course is designed for developers with Java or Scala experience who are interested in building data processing applications using Apache Flink.

Course Objectives

• Gain a comprehensive understanding of Apache Flink's architecture and internals, including its distributed processing model and state management mechanisms.

• Write data processing applications using Flink's core APIs:

  • DataSet API: Efficiently handle batch data for large-scale data transformations and aggregations.

  • DataStream API: Build real-time streaming pipelines that continuously process incoming data streams with low latency.

  • Table API: Leverage a declarative SQL-like syntax for concise data manipulation, simplifying development and improving readability.

• Leverage Flink's capabilities for both batch and real-time data processing, understanding the trade-offs between these approaches.

• Implement fault tolerance mechanisms for robust data pipelines, ensuring data consistency even in case of failures through techniques like checkpoints and state snapshots.

• Explore how Flink integrates with other big data tools like Hadoop, YARN, and Kafka for seamless data processing workflows.

• Understand the advantages of Flink compared to other processing frameworks, such as its high performance, exactly-once processing guarantees, and unified batch and streaming capabilities.

Course Length: 4 days

Course Outline

Introduction to Apache Flink

• Flink Overview: Dive into the world of Apache Flink, exploring its core functionalities, use cases, and its position within the big data landscape.

• Architecture and Internals: Unveil Flink's distributed processing architecture, including its job managers, task managers, and dataflow execution model. Gain insights into state management strategies for handling both transient and persistent data across processing tasks.

• Flink vs. Other Frameworks: Compare and contrast Flink with other big data processing frameworks, highlighting its strengths in terms of performance, scalability, and unified programming model for batch and streaming.

Batch Processing with DataSet API

• DataSet API Fundamentals: Master the core concepts of the DataSet API, including distributed datasets, transformations (e.g., filtering, mapping, grouping), and actions (e.g., counting, reducing, writing to sinks).

• Iterations and Delta Operations: Learn how to perform iterative computations on datasets efficiently using techniques like bulk iterations and delta operations, which optimize updates on large datasets.

Stream Processing with DataStream API

• Real-time Stream Processing: Demystify the concept of continuous data streams and understand how Flink processes them with low latency. Explore techniques for windowing and event-time processing to handle time-based operations on streaming data.

• Exactly-Once Processing: Grasp the concept of exactly-once processing guarantees in Flink's streaming engine, ensuring data consistency even if failures occur during processing.

• Micro-Batching Techniques: Explore micro-batching as an alternative approach for stream processing, understanding its trade-offs compared to fully continuous processing.

• Fault Tolerance Mechanisms: Implement robust stream processing applications using Flink's fault tolerance mechanisms, including checkpoints and state snapshots that allow recovery from failures without data loss.

Table API and Advanced Topics

• Declarative Power of the Table API: Leverage the Flink Table API, a high-level abstraction that allows you to express data transformations using a declarative SQL-like syntax. Explore the Table API's capabilities for joining data streams, filtering, aggregation, and windowing operations.

• Machine Learning in Flink: Discover how Flink can be used for machine learning tasks on data streams. Explore techniques for integrating machine learning models into streaming pipelines for real-time predictions and anomaly detection.

• Integration with Big Data Ecosystem: Learn how to connect Flink with other big data tools like Hadoop, YARN, and Kafka for seamless data processing workflows. Explore how to read data from Hadoop Distributed File System (HDFS), leverage YARN for cluster resource management, and integrate with Kafka for real-time data ingestion.

• Course Wrap-up: Recap the key concepts covered throughout the course, including best practices for building robust and scalable data pipelines with Apache Flink. Explore additional resources for further learning and explore advanced topics like Flink's state management in more depth.