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Computer Science
Stream Processing
Streaming Data Processing with Apache Kafka and KSQL
1. Introduction to Stream Processing
2. Fundamentals of Apache Kafka
3. Introduction to ksqlDB
4. Setting Up the Environment
5. ksqlDB Data Definition Language (DDL)
6. Querying and Transforming Data
7. Aggregations and Windowing
8. Joining Streams and Tables
9. Advanced ksqlDB Features
10. Building Real-Time Applications
11. Operations and Production Considerations
ksqlDB Data Definition Language (DDL)
Working with Streams
CREATE STREAM Statement
Syntax and Options
WITH Clause Parameters
Stream Configuration
Defining Columns and Data Types
Supported Data Types
Column Constraints
Nullable vs. Non-Nullable Columns
Specifying Data Format
JSON Format
AVRO Format
PROTOBUF Format
DELIMITED Format
Specifying the Backing Kafka Topic
Linking Streams to Topics
Auto-Creation of Topics
Topic Naming Conventions
CREATE STREAM AS SELECT (CSAS)
Creating Derived Streams
Query Examples
Persistent Query Creation
Working with Tables
CREATE TABLE Statement
Syntax and Options
WITH Clause Parameters
Table Configuration
Defining Primary Keys
Importance of Primary Keys
Key Constraints
Composite Keys
CREATE TABLE AS SELECT (CTAS)
Creating Derived Tables
Query Examples
Materialized View Creation
Managing Metadata
DESCRIBE
Stream Schema Inspection
Table Schema Inspection
Extended Descriptions
SHOW STREAMS
Listing Existing Streams
SHOW TABLES
Listing Existing Tables
SHOW TOPICS
Listing Kafka Topics
DROP STREAM
Deleting Streams
Impact on Underlying Topics
DROP TABLE
Deleting Tables
Cleanup Considerations
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4. Setting Up the Environment
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6. Querying and Transforming Data