Nice intro by the author, but how are the parts connected to each other? We can dive into it a bit more for a clearer glimpse before we begin our journey in this section:)

Step Snap 1: [Stream Processing Overview]

Stream processing is like a flowing river of data - instead of collecting data in a lake (batch processing), we handle it as it flows by. Here's how the key components work together:

Core Concepts

• Stream Processing: Continuous processing of unbounded data in real-time
• Kafka: Central message bus that acts as the "nervous system" connecting all components

Data Flow Architecture

                     ┌─────────────┐
 ┌─────────┐         │   APACHE    │          ┌──────────────┐
 │         │ writes  │   KAFKA     │  reads   │              │
 │PRODUCERS├────────►│  ┌───────┐  ├─────────►│  CONSUMERS   │
 │         │         │  │Topics │  │          │              │
 └─────────┘         │  │Partns.│  │          └──────┬───────┘
      │              │  └───────┘  │                 │
      │              └─────────────┘                 │
      │                     ▲                        │
      │                     │                        │
      │                     │                        │
      ▼                     ▼                        ▼
┌──────────────┐     ┌─────────────┐         ┌──────────────┐
│  DATA        │     │   SCHEMA    │         │ PROCESSING   │
│  SOURCES     │     │  REGISTRY   │         │ FRAMEWORKS   │
│  - Sensors   │     │  - Data     │         │ - Kafka      │
│  - Logs      │     │    Formats  │         │   Streams    │
│  - Apps      │     │  - Versions │         │ - Spark      │
└──────────────┘     └─────────────┘         │   Streaming  │
                           ▲                 └──────────────┘
                           │                        │
                           ▼                        ▼
                     ┌─────────────┐         ┌──────────────┐
                     │  KAFKA      │         │ APPLICATIONS │
                     │  CONNECT    │         │ - Analytics  │
                     │  - Source   │         │ - Alerts     │
                     │  - Sink     │         │ - Dashboards │
                     └─────────────┘         └──────────────┘

Kafka Architecture

• Topics & Partitions: Data organized into categories (topics) and split into chunks (partitions) for parallelism
• Retention & Replication: Data can be stored for configured periods and duplicated for fault tolerance

Processing Components

• Producers: Systems that generate data and send to Kafka
• Consumers: Applications that read and process the streaming data
• Kafka Streams API: For building stream processing applications (Java examples in course)
• Spark Streaming: Alternative processing framework (Python examples in course)

Advanced Features

• Schema Registry: Ensures data consistency and compatibility
• Kafka Connect: Pre-built connectors for common data sources/sinks
• KSQL DB: SQL interface for stream processing