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Mastering Flink SQL onConfluent Cloud
Unlock the Potential of Real-Time Data Processing

Flink SQL on Confluent Cloud enables robust stream processing
by combining the power of Apache Flink with the scalability andreliability of Confluent Cloud. This comprehensive training isfocused on using Flink SQL within Confluent Cloud for stream
processing.
The 2-day course is focused on:•Understanding the fundamentals of Apache Flink and its
relevance to stream processing•Writing and executing Flink SQL queries on ConfluentCloud•Differentiating between streaming and batch processing•

Working with dynamic tables and understand stream-tableduality•

Managing time attributes and windows for effective
stream processing•

Performing complex windowed aggregations in real-timewith Flink SQL•

Utilizing Flink SQL for efficient joining of streaming data•

Appling pattern-matching techniques to identify complex
event sequences

Who Should Attend?
This course is designed for SQL practitioners who want toextend their skills to stream processing using Flink SQL onConfluent Cloud. It is ideal for data engineers, analysts, anddevelopers who are familiar with SQL and need to apply it toreal-time data streams.
This 2-day course aims to enable participants to efficiently
utilize Flink SQL for stream processing tasks, leveraging
the capabilities of Confluent Cloud to manage and process
streaming data effectively.

Prerequisites
This 200-level course is recommended for participants whohave:•Familiarity with SQL syntax and operations.

•Basic understanding of Kafka (topics & partitions) andstream processing

Hands-on Training
The hands-on lab exercises in this course include:

Participants will learn to utilize Flink SQL to process real-time•

Setting up Flink SQL environments on Confluent Clouddata streams, create dynamic tables, manage time windows,•Writing and executing Flink SQL queries•

Creating and managing dynamic tables•

Implementing time windows and time-based functions•Performing aggregations and joins on streaming data•

Developing pattern-matching queries to detect event
patterns in data streams

and perform complex queries and pattern matching. The course
demonstrates how Flink SQL can transform raw data into
actionable insights within a fully managed cloud environment.
By the end of this course, participants will have a solid
understanding of how to leverage Flink SQL for stream
processing within Confluent Cloud, enabling them to transform
real-time data streams into meaningful insights.

© 2024 Confluent, Inc. | Confluent.io1

Datasheet | 2 -Day Deep Dive

Topics in Detail
Prerequisite Fundamental course
Module 01 Introduction to Flink• 06a. Introduction to Joins• 06b. Regular Joins• 06c. Optimized Joins• 06d. Window Joins• 06e. Other Type of Joins

Module 07 Pattern Matching• 07a. Introduction• 07b. Understanding the Query:• 01a. Origin of Stream Processing• 01b. What is Apache Flink?
• 01c. Apache Flink’s APIs
• 01d. Flink Job & Topology

Module 02 Getting Started
with Flink SQL• 02a. Why Flink SQL for Stream
Processing?
• 02b. Flink SQL Syntax• 02c. Streaming vs. Batch
Processing• 02d. Flink SQL on Confluent Cloud

Module 03 Working withDynamic Tables• 03a. Traditional SQL vs. Streaming
SQL• 03b. Stream-Table Duality• 03c. Dynamic Table Creation• 03d. Column Types• 03e. Stateless vs. Stateful Operators

Module 04 Time & Windows•••••

Module 05 Aggregations

Module 06 Joins•••••••••••••

Partitioning
Order of Events
DEFINEMEASURES
Define a PatternOutput ModeAfter Match Strategy• 07c. Pattern Navigation• 07d. Time Attributes• 07e. Examples

04a. Event Time vs. Processing
Time
04b. Time Attributes vs.
Timestamps
04c. Watermarks
04d. Windows
04e. Time Functions & Data Types

05a. Overview
05b. GROUP BY vs. OVER
05c. Aggregate Functions
05d. Special Aggregation Queries
05e. Additional Aggregation
Options
05f. Aggregations: ImportantConsiderations© 2024 Confluent, Inc. | Confluent.io2