Real-Time Data Processing with PyFlink and Iceberg

Real-Time Data Processing with PyFlink and Iceberg

Harness the Power of Stream Processing and Advanced Table Formats for Modern Data Architectures in 2 days

Data doesn't sleep, and the companies that win don't either. Apache Flink, now accessible through PyFlink, has revolutionized the game by letting you process massive streams of data the moment it arrives—whether it's flowing non-stop or coming in batches. It's fast, reliable, Python-friendly, and it's what the most innovative teams are using to stay ahead.

Enter Apache Iceberg: the missing piece that brings order to data lake chaos. It keeps your massive datasets consistent and manageable, even when you're working at enterprise scale.

Our two-day workshop cuts through the hype and gets your hands dirty with both technologies using Python. You'll walk away knowing how to build actual real-time data systems that solve real problems—the kind that make executives notice and competitors worry.

Learning Outcomes:

By the end of this course, participants will be able to:

• Comprehend the core concepts and architecture of Apache Flink and its role in stream processing.
• Develop and deploy data processing pipelines using PyFlink's DataStream API.
• Implement stateful stream processing and manage event time semantics effectively using Python.
• Integrate PyFlink with Apache Iceberg to manage large analytic tables efficiently.
• Utilize Flink SQL through PyFlink for querying and manipulating data within Iceberg tables.
• Apply best practices for building resilient and scalable real-time data applications.

Prerequisites:

Participants should have:

• Proficiency in Python programming language.
• Fundamental understanding of distributed systems and parallel processing concepts.
• Basic knowledge of data processing frameworks such as Apache Hadoop or Apache Spark.
• Familiarity with SQL and relational database concepts.

Detailed Course Outline:

1. Introduction to Stream Processing and Apache Flink
   1. The Evolution of Data Processing: From Batch to Stream
   2. Key Concepts in Stream Processing
   3. Overview of Apache Flink
      1. Flink's Position in the Big Data Ecosystem
      2. Core Features and Capabilities
   4. Use Cases and Industry Applications
2. Apache Flink Architecture and Runtime
   1. Flink's Distributed Architecture
      1. JobManager and TaskManager Roles
      2. Execution Model and Dataflow
   2. Fault Tolerance Mechanisms
      1. Checkpointing and State Snapshots
      2. Exactly-Once Semantics
   3. Deployment Modes
      1. Standalone Cluster
      2. YARN and Kubernetes Integrations
3. Getting Started with PyFlink's DataStream API
   1. Setting Up the Python Development Environment for PyFlink
   2. Building a Simple PyFlink Application
      1. Defining Data Sources and Sinks in Python
      2. Applying Transformations with PyFlink
   3. Understanding Streams and Transformations
      1. Stateless vs. Stateful Transformations
      2. Keyed Streams and Partitioning
4. Stateful Stream Processing with PyFlink
   1. The Importance of State in Stream Processing
   2. Managing Operator State and Keyed State in PyFlink
   3. Implementing Process Functions
      1. Timers and Event Time Processing
   4. State Backends and Their Configurations
      1. Memory State Backend
      2. RocksDB State Backend
5. Time and Windowing in PyFlink
   1. Understanding Event Time vs. Processing Time
   2. Generating and Assigning Timestamps in Python
   3. Watermarks and Their Role in Event Time Processing
   4. Window Operators and Functions in PyFlink
      1. Tumbling Windows
      2. Sliding Windows
      3. Session Windows
   5. Late Data Handling Strategies
6. Integrating PyFlink with Apache Iceberg
   1. Introduction to Apache Iceberg
      1. Motivation and Key Features
      2. Comparison with Traditional Table Formats
   2. Setting Up Iceberg with PyFlink
      1. Configuring Iceberg Catalogs in Python
      2. Creating and Managing Iceberg Tables
   3. Performing Data Operations
      1. Inserting and Deleting Records using PyFlink
      2. Schema Evolution and Partitioning
   4. Querying Iceberg Tables with Flink SQL using PyFlink
      1. Writing and Executing SQL Queries
      2. Optimizing Query Performance