Kafka

• Big Data
• Need for data pipelines


  • Stream processing requires processing of events
  • Events are processed on the server

  
• Kafka's core strengths that address data pipeline challenges


  • Real-time Data Ingestion and Processing
  • Reliable Data Persistence
  • Scalable Data Distribution
  • Decoupling Producers and Consumers
  • Flexible Integration
• Real-time Analytics and Decision-Making


  Enables immediate analysis of incoming data for faster insights and actions
• Stream Processing Applications


  Powers applications that continuously process and react to data streams in real time
• Event-Driven Architectures


  Facilitates event-driven systems where components react to events as they occur
• Microservices Communication


  Provides a resilient and scalable messaging backbone for communication between microservices
• Data Integration and Synchronization


  Streamlines data movement between different systems, ensuring consistency and timeliness
• Kafka's unique capabilities make it an essential tool for building modern data pipelines that handle real-time data flows effectively, ensure reliable data delivery and persistence, scale to meet growing data volumes and processing needs, and provide flexibility and integration with diverse systems
• Kafka empowers businesses to unlock the full potential of their data for real-time insights, decision-making, and continuous innovation
• We often have to read from multiple data sources
• We can connect multiple clients each over a pool of connections
• We might have multiple servers on which to process the same data
• Use case: stock trading – may have different analysts wanting to analyze the behavior
• Kafka can have an intermediary that connects different sources and multiple backends
• Kafka decouples the pipeline so that producers and consumers do not need to know about each other
• Publisher


  Publishes messages to the Communication Infrastructure
• Subscriber


  Subscribes to a category of messages
• The producer plays a crucial role in the Kafka data streaming ecosystem, acting as the source of data input
• Responsibilities of the Producer


  • Data Ingestion
  • Data Serialization
  • Partitioning
  • Acks and Retries
  • Producer Metrics Monitoring
• Key functionalities of the Producer


  • Producer API
  • Batching
  • Compression
  • Transactions (optional)
• Importance of the Producer


  • Initiates data flow
  • Performance and Scalability
  • Reliable Delivery
• Responsibilities of the Consumer


  • Topic Subscription
  • Data Fetching
  • Data Deserialization
  • Data Processing
  • Offset Management
  • Error Handling
• Key functionalities of the Consumer


  • Consumer API
  • Consumer Groups
  • Commit Strategies
  • Rebalancing
• Importance of the Consumer


  • Data Utilization
  • Scalability and Availability
  • Flexibility and Integration
• Kafka's communication infrastructure thrives on a distributed, high-performance messaging system with distinct components that work together seamlessly
• Key elements of Kafka's communication infrastructure

  • Producers
  • Topics
  • Brokers
  • Consumers
  • ZooKeeper