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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
Aggregations and Windowing
Statefulness in ksqlDB
Materialized Views
Definition and Use Cases
View Maintenance
State Stores
Local State Management
Fault Tolerance for State
State Store Types
Aggregating Data
The GROUP BY Clause
Syntax and Usage
Grouping Keys
Multiple Column Grouping
Aggregate Functions
COUNT
COUNT_DISTINCT
SUM
AVG
MIN
MAX
COLLECT_LIST
COLLECT_SET
The HAVING Clause
Filtering Aggregated Results
Post-Aggregation Conditions
Windowing Concepts
Purpose of Windowing
Handling Unbounded Data
Time-Based Aggregation
Memory Management
Types of Windows
Tumbling Windows
Fixed-Size Non-Overlapping
Use Cases
Hopping Windows
Fixed-Size Overlapping
Advance and Size Parameters
Session Windows
Dynamic Inactivity-Based
Session Gap Configuration
Grace Period for Late-Arriving Data
Handling Out-of-Order Events
Grace Period Configuration
Window Retention
Window Cleanup
Retention Policies
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6. Querying and Transforming Data
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8. Joining Streams and Tables