Streaming Data Processing with Apache Kafka and KSQL

Streaming Data Processing with Apache Kafka and KSQL is a powerful paradigm for analyzing data in real-time, leveraging Apache Kafka as the distributed, fault-tolerant event streaming platform to ingest and store continuous data flows. Layered on top, KSQL (now ksqlDB) provides an interactive SQL interface that allows developers and analysts to filter, transform, aggregate, and join these data streams on the fly, without writing complex application code. This combination simplifies the creation of real-time applications, such as monitoring dashboards, anomaly detection systems, and dynamic pricing engines, by making the powerful capabilities of stream processing accessible through familiar SQL syntax.

1. Introduction to Stream Processing
   1. Core Concepts of Data Streaming
      1. Definition of Data Streaming
      2. Data-in-Motion vs. Data-at-Rest
         1. Characteristics of Data-in-Motion
         2. Characteristics of Data-at-Rest
         3. Use Cases for Each
      3. Events and Event Streams
         1. Definition of an Event
         2. Event Streams as Sequences
         3. Event Sourcing
      4. Unbounded vs. Bounded Data
         1. Characteristics of Unbounded Data
         2. Characteristics of Bounded Data
         3. Implications for Processing
   2. Stream Processing vs. Batch Processing
      1. Definition of Stream Processing
      2. Definition of Batch Processing
      3. Latency and Throughput Considerations
         1. Low-Latency Requirements
         2. Throughput Considerations
         3. Trade-offs Between Latency and Throughput
      4. Use Cases for Each Paradigm
         1. Real-Time Analytics
         2. Data Warehousing
         3. ETL Pipelines
         4. Fraud Detection
         5. IoT Data Processing
   3. Key Terminology
      1. Event Time vs. Processing Time
         1. Definition of Event Time
         2. Definition of Processing Time
         3. Watermarks and Time Semantics
         4. Clock Skew and Time Synchronization
      2. Statefulness in Stream Processing
         1. Stateless Processing
         2. Stateful Processing
         3. Use Cases for Statefulness
         4. State Management Challenges
      3. Windowing
         1. Purpose of Windowing
         2. Types of Windows Overview
         3. Window Triggers
         4. Late Data Handling