Study Notes 6.1-2: Introduction to Stream Processing

1. Overview of Stream Processing

• Definition: Continuous, real-time processing of data as it arrives.
• Importance: Enables real-time analytics, event-driven applications, and prompt reactions to data (e.g., fraud detection, live monitoring).

2. Introduction to Kafka

• Role in Stream Processing:
  • Acts as a high-throughput, distributed message broker.
  • Facilitates the ingestion, buffering, and distribution of streaming data.
• Core Concepts:
  • Kafka Topics: Logical channels where messages are published.
  • Message Properties: Characteristics that define how data is handled within streams.

3. Key Kafka Configuration Parameters

• Partitioning:
  • Divides data into segments (partitions) to enable parallel processing and load balancing.
• Replication:
  • Ensures fault tolerance by copying data across multiple brokers.
• Retention Time:
  • Determines how long messages are stored before being purged.
• Other Settings:
  • Configuration parameters specific to Kafka that influence performance and reliability.

4. Kafka Producers and Consumers

• Kafka Producers:
  • Applications or scripts that send (produce) messages to Kafka topics.
  • Can be implemented programmatically using various languages.
• Kafka Consumers:
  • Applications that subscribe to topics to receive (consume) and process messages.
  • Support programmatic consumption for integrating with downstream systems.

5. Data Partitioning in Stream Processing

• Purpose:
  • Enhances scalability by distributing data across partitions.
  • Improves parallel processing, leading to better performance.
• Example:
  • Partitioning strategies determine how data is distributed among consumers.

6. Practical Examples & Language-Specific Implementations

• Java Examples:
  • Kafka Streams examples, demonstrating how to work with Kafka in a Java environment.
• Python Examples:
  • Spark Streaming examples using Python for those who prefer Python over Java.
• Key Takeaway:
  • The choice of language and framework can depend on team expertise and project requirements.

7. Schema and Its Role in Stream Processing

• What is a Schema?
  • A defined structure for data (e.g., field types, format) that ensures consistency.
• Importance:
  • Helps in managing data quality and enables smooth integration between systems.
  • Facilitates schema evolution when data structures change over time.

8. Kafka Connect and Related Tools

• Kafka Connect:
  • A framework for connecting Kafka with external systems (databases, file systems, etc.) without writing custom code.
• Additional Tools:
  • Brief mention of other tools (e.g., “case equal DB”) that integrate with Kafka, highlighting the ecosystem available for stream processing.

1. Introduction to Data Exchange

• Definition: Data exchange is the process of transferring data from one source (producer) to another (consumer) using various communication channels.
• Real-World Analogy – Postal Service:
  • Just like writing a letter and sending it through the postal service, data can be written and sent to a designated receiver.
  • This simple analogy emphasizes that data exchange involves a sender, a transport medium, and a receiver.

2. Data Exchange in Modern Computing

• Computer Communication:
  • In today’s digital world, data exchange is often handled through APIs such as REST, GraphQL, and webhooks.
  • These methods ensure that data flows from one system to another reliably and efficiently.
• Notice Board Analogy:
  • Producer: Imagine a person posting a flyer on a community notice board.
    • The flyer contains information (data) meant for a specific audience.
  • Consumers: Passersby (or subscribers) who read the flyer can act on it, ignore it, or pass it along.
  • Topic-based Distribution:
    • If a flyer is posted on a board dedicated to a specific subject (e.g., Kafka, Spark, stream processing, Big Data), only those interested in that subject (consumers subscribed to that topic) will take notice.

3. Stream Processing Explained

• Traditional (Batch) Data Exchange:
  • In many systems, data exchange happens in batches—data is collected over a period (minutes, hours, or even days) and then processed.
  • Examples include receiving emails or checking a physical notice board when passing by.
• Stream Processing:
  • Real-Time Data Exchange:
    • In stream processing, data is exchanged almost immediately after it is produced.
    • A producer sends a message to a topic (e.g., a Kafka topic), and that message is instantly available to any consumer subscribed to that topic.
  • Key Benefit:
    • The reduced delay compared to batch processing means data is processed in near-real time, enabling faster decision making.

4. Understanding "Real-Time" in Stream Processing

• Not Instantaneous:
  • Real-time processing does not mean zero latency or instantaneous delivery (i.e., not at the speed of light).
  • There is typically a few seconds of delay, which is significantly less than the delays common in batch processing.
• Comparison to Batch Processing:
  • Batch Processing:
    • Data is consumed and processed every minute, hour, or even later.
  • Stream Processing:
    • Data flows continuously, allowing for almost immediate processing.

5. Practical Examples with Kafka and Spark

• Kafka Topics:
  • A producer writes data to a Kafka topic.
  • Consumers subscribed to that topic receive the data in near-real time.
• Spark Topics:
  • Similar to Kafka, in some examples, data might be sent to a Spark topic where consumers process the stream in real time.
• Programming Aspect:
  • Both Kafka and Spark provide APIs and libraries for programmatically producing and consuming data, making it easier to build real-time applications.