Apache Kafka Overview and Key Features
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Questions and Answers

What feature of Apache Kafka ensures data reliability during hardware failures?

  • Horizontal scaling
  • Low latency processing
  • Message replication (correct)
  • Decoupled architecture

    • Which of the following best describes Kafka's messaging model?

  • Sequential processing only
  • Unidirectional messaging
  • Point-to-point only
  • Publish-subscribe and point-to-point (correct)

    • How does Kafka handle scalability in message processing?

  • Through vertical scaling only
  • By using partitions across clusters (correct)
  • By limiting message size
  • By reducing replication factors

    • Which of the following statements about Kafka's durability is correct?

    <p>Messages are written to disk based on user-defined policies</p> Signup and view all the answers

    Why is Kafka considered integration-friendly?

    <p>It works well with external systems using Kafka Connect</p> Signup and view all the answers

    In which scenario does Kafka excel as a streaming platform?

    <p>For real-time data processing needs</p> Signup and view all the answers

    Which company is NOT known for using Apache Kafka?

    <p>Facebook</p> Signup and view all the answers

    What is one key advantage of using a decoupled architecture in Kafka?

    <p>It allows independent operation of components</p> Signup and view all the answers

    Which library does Kafka provide for stream processing?

    <p>Kafka Streams</p> Signup and view all the answers

    How does Kafka support fault tolerance within its architecture?

    <p>Through data replication and leader-followers</p> Signup and view all the answers

    What role does a Kafka Broker play in the Kafka architecture?

    <p>Manages incoming and outgoing messages with specific retention settings</p> Signup and view all the answers

    How does a Producer determine which partition to send messages to?

    <p>Based on custom logic or random assignment</p> Signup and view all the answers

    What is a key feature of Consumer Groups in Kafka?

    <p>Each partition is assigned to only one consumer in the group</p> Signup and view all the answers

    What does Kafka guarantee regarding message ordering?

    <p>Ordering is preserved only within a single partition</p> Signup and view all the answers

    What is ZooKeeper primarily used for in the Kafka system?

    <p>For cluster management and leader election</p> Signup and view all the answers

    What happens when a broker fails in Kafka?

    <p>Consumers can still process messages from other brokers</p> Signup and view all the answers

    What is the significance of partitioning in Kafka?

    <p>It promotes distributed message handling and parallelism</p> Signup and view all the answers

    What is one of the responsibilities of a Producer in Kafka?

    <p>Publishing messages to specific Kafka topics</p> Signup and view all the answers

    What feature allows consumers in Kafka to resume from the last read point?

    <p>Offset management</p> Signup and view all the answers

    How does Kafka handle message delivery assurance?

    <p>Brokers optionally provide acknowledgments on receipt</p> Signup and view all the answers

    Study Notes

    Apache Kafka Overview

    • Kafka is a distributed streaming platform designed for real-time data processing.
    • It enables publish-subscribe messaging and is fault-tolerant, scalable, and high-throughput.

    Kafka Key Features

    • High Throughput and Scalability:
      • Supports processing millions of messages per second with low latency.
      • Achieves horizontal scaling across clusters using partitions.
    • Fault Tolerance:
      • Message replication ensures data reliability even with hardware or node failures.
      • Leaders and followers architecture ensures continuous operation.
    • Durability:
      • Writes messages to disk and retains them based on user-defined policies.
    • Flexible Messaging Model:
      • Supports both publish-subscribe and point-to-point messaging, suitable for various use cases.
    • Stream Processing:
      • Provides libraries like Kafka Streams for real-time data transformation and aggregation.
    • Decoupled Architecture:
      • Producers and consumers operate independently, enabling seamless integration across distributed systems.
    • Integration-Friendly:
      • Works well with external systems using Kafka Connect, supporting plugins for databases, file systems, and more.

    Kafka in the Big Data Ecosystem

    • Central Data Hub:
      • Acts as a backbone for managing and transferring real-time data between various components in a big data stack.
    • Key Integrations:
      • Apache Hadoop/Spark: Used to feed raw data streams for batch or stream processing.
      • Machine Learning Pipelines: Streams data for real-time model updates and predictions.

    Why Kafka is Preferred

    • Designed for Real-Time Streaming:
      • Excels in scenarios needing high throughput, fault tolerance, and real-time data processing.
    • Integration with Big Data Ecosystem:
      • Works seamlessly with systems like Apache Spark, Flink, and Hadoop for building advanced data pipelines.
    • Adoption by Industry Leaders:
      • Companies like LinkedIn, Netflix, Uber, and Spotify use Kafka for scalability, speed, and reliability in mission-critical systems.

    Who Uses Kafka?

    • LinkedIn: Activity data and operational metrics.
    • Twitter: Part of Storm – stream processing infrastructure.
    • Many other companies including Spotify, Uber, Tumbler, Goldman Sachs, PayPal, Box, Cisco, CloudFlare, DataDog, LucidWorks, MailChimp, Netflix, and others.

    Kafka in LinkedIn

    • More than 1 petabyte of data in Kafka.
    • Over 1.2 trillion messages per day.
    • Thousands of data streams.
    • Source of all data warehouse and Hadoop data.
    • Over 300 billion user-related events per day.

    Kafka in Fortune 500's

    • Over 35% of Fortune 500 companies use Kafka (2018).
    • Strong presence in various sectors: Travel, Global Banks, Insurance, Telecom.

    Kafka Advantages

    • Low Latency
    • High Throughput
    • Fault Tolerance
    • Scalability
    • Batch Handling
    • Real-time Handling
    • Message Broker Capabilities:
    • Distributed By default
    • Durable and by default Persistent
    • Consumer Friendly

    Kafka Disadvantages

    • Issues with Message Tweaking
    • No Complete Set of Monitoring Tools
    • Not Support Wildcard Topic Selection
    • Behaves Clumsily
    • Lacks Some Messaging Paradigms
    • Lack of Pace
    • Performance Reduction

    Kafka Architecture - Brokers

    • Kafka Broker is a server that manages incoming and outgoing messages.
    • Key Features:
      • Message Storage: Retains messages for a specified retention period.
      • Request Handling: Handles read and write requests from producers and consumers.
      • Scalability: Kafka clusters consist of multiple brokers for handling large workloads.
      • Each broker has a unique ID and coordinates with others in a distributed system.

    Kafka Architecture - Producers

    • Client applications responsible for publishing messages to Kafka topics.
    • Key Features:
      • Partitioning: Sends messages to specific partitions based on custom logic/random assignment.
      • Acknowledgment: Can request acknowledgments from brokers for data delivery.
      • Compression: Supports message compression for reduced storage and network usage.

    Kafka Architecture - Consumers

    • Applications subscribing to topics and processing messages.
    • Key Features:
      • Consumer Groups: Multiple consumers in a group, with Kafka ensuring each partition is assigned to only one consumer.
      • Offset Management: Tracks message offsets, enabling sequential data processing or resuming from the last read point.
      • Pull Model: Consumers request data from brokers.

    Kafka Architecture - Partitions

    • Topics are divided into partitions for scalability and parallelism.
    • Key Features:
      • Parallel Processing: Multiple partitions enable distributed message handling.
      • Ordering: Guarantees order within a single partition, not across partitions.
      • Load Distribution: Producers distribute messages for even workload handling across partitions.

    ZooKeeper's Role

    • Distributed coordination service used by Kafka.
    • Key Roles:
      • Cluster Management: Maintains metadata about brokers, topics, and partitions.
      • Leader Election: Facilitates leader election for partitions and brokers.
      • Configuration Management: Tracks/propagates configuration changes across the cluster enabling fault tolerance and managing broker failures.

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    Related Documents

    Apache Kafka Overview PDF

    Description

    Explore the essentials of Apache Kafka, a distributed streaming platform built for real-time data processing. This quiz covers its high throughput, fault tolerance, and flexible messaging model, along with details about stream processing and durability.

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