Kafka - Popular Use Cases

1. Introduction to Apache Kafka

• Kafka started as a tool for log processing at LinkedIn.
• It has evolved into a versatile distributed event streaming platform.
• Its design utilizes immutable append-only logs with configurable retention policies, making it useful beyond its original purpose.

2. Log Analysis

• Initially designed for log processing, Kafka now supports centralized, real-time log analysis.
• Modern Log Analysis:
  • It involves the centralization of logs from distributed systems.
  • Kafka can ingest logs from multiple sources like microservices, cloud platforms, and applications, handling high volume with low latency.
• Integration with ELK Stack:
  • Kafka works well with tools like Elasticsearch, Logstash, and Kibana (ELK Stack).
  • Logstash pulls logs from Kafka, processes them, and sends them to Elasticsearch, while Kibana provides real-time visualization.

3. Real-time Machine Learning Pipelines

• Purpose: Modern ML systems need to process large data volumes quickly and continuously.
• Kafka serves as the central nervous system for ML pipelines, ingesting data from various sources (user interactions, IoT devices, financial transactions).
• Example: In fraud detection systems, Kafka streams transaction data to ML models for instant identification of suspicious activity.
• Integration with Stream Processing Frameworks:
  • Works seamlessly with Apache Flink and Spark Streaming for complex computations.
  • Kafka Streams, Kafka’s native processing library, allows scalable, fault-tolerant stream processing.

4. Real-time System Monitoring and Alerting

• Difference from Log Analysis: It’s about immediate, proactive tracking of system health and alerting.
• Kafka acts as a central hub for metrics and events across the infrastructure (application performance, server health, network traffic).
• Real-time Processing:
  • Kafka enables continuous analysis and real-time aggregation, anomaly detection, and alerting.
• Kafka’s Persistence Model:
  • Allows time-travel debugging by replaying metric streams for incident analysis.

5. Change Data Capture (CDC)

• Definition: A method to track and capture changes in source databases.
• Kafka acts as a central hub for streaming database changes to downstream systems.
• Process:
  • Source databases generate transaction logs that record data modifications.
  • Kafka stores these change events in topics, allowing independent consumption.
• Kafka Connect:
  • A framework used to build and run connectors, facilitating data movement between Kafka and other systems (e.g., Elasticsearch, databases).

6. System Migration

• Functionality: Kafka acts as a buffer and translator between old and new systems during migrations.
• Migration Patterns:
  • Supports patterns like the Strangler Fig and Parallel Run with comparison.
• Kafka allows message replay, aiding data reconciliation and consistency during migrations.
• Safety Net:
  • Supports running old and new systems in parallel for easy rollback and detailed comparison.