IBM Mastering Flink SQL on Confluent Cloud - Essentials
- Référence MASTERFLINKG
- Durée 1 Jour
Modalité pédagogique
Classe inter en présentiel Prix
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Résumé
Haut de pageLearn to design, query, and analyze real-time streaming data using Flink SQL on Confluent Cloud, enabling you to transform live events into actionable business insights.
Master real-time stream processing with Flink SQL on Confluent Cloud. This hands-on course teaches you to process live data streams, build dynamic tables, and run complex time-windowed queries- equipping you to turn raw streaming data into immediate, actionable insights within a fully managed cloud environment.
NB: This course is included in FLINKCC2G for the first 2 days.
Updated Sept. 2026
Prochaines dates
Haut de pagePublic
Haut de pageThis course is designed for SQL practitioners who want to extend their skills to stream processing using Flink SQL on Confluent Cloud. It is ideal for data engineers, analysts, and developers who are familiar with SQL and need to apply it to real-time data streams.
Objectifs de la formation
Haut de pageAfter this course participants should be able to:
- Understanding the fundamentals of Apache Flink and its relevance to stream processing
- Writing and executing Flink SQL queries on Confluent Cloud
- Differentiating between streaming and batch processing
- Working with dynamic tables and understand streamtable duality
- Managing time attributes and windows for effective stream processing
- Performing complex windowed aggregations in real-time with Flink SQL
- Utilizing Flink SQL for efficient joining of streaming data
- Appling pattern-matching techniques to identify complex event sequences
Programme détaillé
Haut de pageModule 1: Introduction to Flink
- Origin of Stream Processing
- What is Apache Flink-
- Apache Flink's APIs
- Flink Job & Topology
- Lab 1: Setting up the lab environment
Module 2: Getting Started with Flink SQL
- Why Flink SQL for Stream Processing-
- Flink SQL Syntax
- Streaming vs. Batch Processing
- Flink SQL on Confluent Cloud
- Lab 2: Working with Flink in Confluent Cloud
Module 3: Working with Dynamic Tables
- Traditional SQL vs. Streaming SQL
- Stream-Table Duality
- Dynamic Table Creation
- Column Types
- Stateless vs. Stateful Operators
- Lab 3: Working with Dynamic Tables
Module 4: Time & Windows
- Event Time vs. Processing Time
- Time Attributes vs. Timestamps
- Watermarks
- Windows
- Time Functions & Data Types
- Lab 4: Using Watermarks and Windows
Module 5: Aggregations
- Overview
- GROUP BY vs. OVER
- Aggregate Functions
- Special Aggregation Queries
- Additional Aggregation Options
- Aggregations: Important Considerations
- Lab 5: Using Aggregations in a Practical Use Case
Module 06: Joins
- Introduction to Joins
- Regular Joins
- Optimized Joins
- Window Joins
- Other Type of Joins
- Lab 6: Exploring Various Types of Joins
Appendix: Pattern Matching
- Introduction
- Understand Query (Partitioning, Order of Events, DEFINE, MEASURES, Define a Pattern, Output Mode, After Match Strategy)
- Pattern Navigation
- Time Attributes
- Examples
Pré-requis
Haut de pageLearners are recommended to have foundational SQL knowledge and a general understanding of data management and cloud computing concepts to maximize the benefits of this course.
Certification
Haut de pageNone
Et après
Haut de pageNone recommended
Bon à savoir
Haut de page- Official course book provided to participants
