Distributed and Cloud Systems Micro Specialization | Week 8: Cloud Applications: MapReduce, Spark, and Apache Kafka by Prakhar Chauhan | Learn Smarter
K12 Students

Academics

AI-Powered learning for Grades 8–12, aligned with major Indian and international curricula.

Academics

Grades

Grade 8 Grade 9 Grade 10 Grade 11 Grade 12

Curriculum

CBSE ICSE IB
Professionals

Professional Courses

Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.

Professional Courses
Games

Interactive Games

Fun, engaging games to boost memory, math fluency, typing speed, and English skills—perfect for learners of all ages.

games
Typing
Memory
Math
English Adventures
Knowledge
Week 8: Cloud Applications: MapReduce, Spark, and Apache Kafka

The chapter covers the core technologies pivotal for processing and managing vast datasets and real-time data in cloud environments, focusing on MapReduce, Apache Spark, and Apache Kafka. It explains the foundational principles of distributed data processing, the evolution of MapReduce to Spark for enhanced performance, and the role of Kafka in constructing scalable and fault-tolerant data pipelines. Understanding these systems is crucial for developing cloud-native applications aimed at big data analytics and machine learning.

Sections

  • 1

    Mapreduce: A Paradigm For Distributed Batch Processing

    MapReduce is a programming model and framework that simplifies the processing of large datasets through distributed computing.

    Learning Practice

  • 1.1

    Mapreduce Paradigm: Decomposing Large-Scale Computation

    The MapReduce paradigm simplifies the processing of large datasets by breaking tasks into manageable units that can be processed in parallel across a distributed infrastructure.

    Learning

  • 1.1.1

    Map Phase

    The Map Phase is a critical component of the MapReduce framework that processes large datasets in parallel by transforming input data into intermediate key-value pairs.

    Learning Practice

  • 1.1.1.1

    Input Processing

    The section covers fundamental technologies in cloud computing for processing large datasets and real-time data streams, focusing on MapReduce, Spark, and Apache Kafka.

    Learning Practice

  • 1.1.1.2

    Transformation

    This section explores key technologies used for processing large datasets in cloud environments, focusing on MapReduce, Spark, and Apache Kafka.

    Learning Practice

  • 1.1.1.3

    Intermediate Output

    This section covers core cloud technologies including MapReduce, Apache Spark, and Kafka, essential for processing large datasets and real-time data streams.

    Learning Practice

  • 1.1.1.4

    Example For Word Count

    This section covers the MapReduce paradigm, emphasizing the Word Count example to illustrate the framework's functionality.

    Learning Practice

  • 1.1.2

    Shuffle And Sort Phase (Intermediate Phase)

    The Shuffle and Sort phase is crucial in the MapReduce paradigm as it organizes intermediate outputs from the Map phase for efficient processing in the Reduce phase.

    Learning Practice

  • 1.1.2.1

    Grouping By Key

    This section discusses the significance of the Grouping by Key phase in the MapReduce framework, particularly during the Shuffle and Sort stage.

    Learning Practice

  • 1.1.2.2

    Partitioning

    Partitioning in the MapReduce paradigm ensures that intermediate data from all Map tasks is distributed evenly among Reducer tasks to enhance efficiency.

    Learning Practice

  • 1.1.2.3

    Copying (Shuffle)

    This section overviews the Copying (Shuffle) phase in the MapReduce programming model, emphasizing its function in ensuring data organization for the reduce phase.

    Learning Practice

  • 1.1.2.4

    Sorting

    The section on sorting focuses on the essential principles and methodologies used in distributed data processing with technologies like MapReduce and Spark.

    Learning Practice

  • 1.1.2.5

    Example For Word Count

    This section provides a detailed overview of the MapReduce paradigm, specifically focusing on how to implement a word count program.

    Learning Practice

  • 1.1.3

    Reduce Phase

    The Reduce Phase in MapReduce aggregates and summarizes the intermediate data produced during the Map phase, transforming it into the final output.

    Learning Practice

  • 1.1.3.1

    Aggregation/summarization

    This section explores the core technologies of MapReduce, Spark, and Apache Kafka in the context of cloud applications, focusing on their roles in processing and managing large datasets.

    Learning Practice

  • 1.1.3.2

    Final Output

    Learning

  • 1.1.3.3

    Example For Word Count

    This section explores the MapReduce paradigm, specifically through the practical application of counting words in a dataset.

    Learning Practice

  • 1.2

    Programming Model: User-Defined Functions For Parallelism

    This section discusses the MapReduce framework, emphasizing its programming model through user-defined Mapper and Reducer functions facilitating distributed parallel processes.

    Learning Practice

  • 1.2.1

    Mapper Function Signature

    This section covers the Mapper function signature in MapReduce, highlighting its role and characteristics.

    Learning Practice

  • 1.2.2

    Reducer Function Signature

    The reducer function signature defines how to aggregate intermediate values into final results in a MapReduce job.

    Learning

  • 1.3

    Applications Of Mapreduce: Batch Processing Workloads

    MapReduce excels in processing large datasets for batch-oriented applications where throughput is prioritized over latency.

    Learning Practice

  • 1.3.1

    Log Analysis

    This section explores the significance of log analysis within the MapReduce programming model for batch processing.

    Learning Practice

  • 1.3.2

    Web Indexing

    Web indexing using MapReduce involves crawling web pages and building an inverted index for search engines effectively.

    Learning Practice

  • 1.3.3

    Etl (Extract, Transform, Load) For Data Warehousing

    ETL is a critical process in data warehousing that involves extracting data from various sources, transforming it for analysis, and loading it into a centralized repository.

    Learning Practice

  • 1.3.4

    Graph Processing (Basic)

    This section focuses on the basics of graph processing including its applications in big data contexts, specifically highlighting how MapReduce can be applied to simple graph computations.

    Learning Practice

  • 1.3.5

    Large-Scale Data Summarization

    The section discusses large-scale data summarization techniques using MapReduce, emphasizing its two-phase processing model and applications.

    Learning Practice

  • 1.3.6

    Machine Learning (Batch Training)

    This section explores the application of MapReduce in batch processing for machine learning by detailing its execution model and key concepts.

    Learning Practice

  • 1.4

    Scheduling In Mapreduce: Orchestrating Parallel Execution

    This section discusses the scheduling and coordination of MapReduce jobs within the Hadoop ecosystem, highlighting the evolution from JobTracker to YARN and the significance of efficient scheduling and fault tolerance.

    Learning Practice

  • 1.4.1

    Historical (Hadoop 1.x) - Jobtracker

    The JobTracker in Hadoop 1.x is a central component responsible for the scheduling and coordinating of MapReduce jobs, previously involving resource management without high availability or scalability.

    Learning Practice

  • 1.4.2

    Modern (Hadoop 2.x+) - Yarn (Yet Another Resource Negotiator)

    YARN decouples resource management and job scheduling, significantly enhancing Hadoop's scalability and efficiency in processing large datasets and managing resources.

    Learning Practice

  • 1.4.2.1

    Resourcemanager

    This section discusses the ResourceManager's role in the YARN architecture for managing resources in cloud environments.

    Learning Practice

  • 1.4.2.2

    Applicationmaster

    This section focuses on the role of the ApplicationMaster within the YARN resource management framework, emphasizing its responsibilities for executing MapReduce jobs.

    Learning Practice

  • 1.4.2.2.1

    Negotiating Resources From The Resourcemanager

    This section discusses the role of ResourceManager in managing resources for MapReduce jobs within the Hadoop eco-system.

    Learning Practice

  • 1.4.2.2.2

    Breaking The Job Into Individual Map And Reduce Tasks

    This section discusses how MapReduce jobs are divided into individual Map and Reduce tasks to optimize distributed computing.

    Learning Practice

  • 1.4.2.2.3

    Monitoring The Progress Of Tasks

    This section discusses the roles and functions related to monitoring the progress of tasks in a cloud-native environment, particularly focusing on Apache Hadoop's MapReduce framework.

    Learning Practice

  • 1.4.2.2.4

    Handling Task Failures

    This section explains how MapReduce ensures fault tolerance and handles task failures within distributed computing environments.

    Learning Practice

  • 1.4.2.2.5

    Requesting New Containers (Execution Slots) From Nodemanagers

    In this section, we explore how the ApplicationMaster requests execution slots (containers) from NodeManagers in the YARN architecture to manage resource allocation effectively for MapReduce tasks.

    Learning Practice

  • 1.4.2.3

    Nodemanager

    The NodeManager is a critical component of the YARN architecture, responsible for managing the resources and execution of tasks on individual nodes in a Hadoop cluster.

    Learning Practice

  • 1.4.3

    Data Locality Optimization

    Data locality optimization is a crucial aspect of distributed data processing systems that aims to minimize network data transfer by scheduling tasks based on the physical location of data.

    Learning Practice

  • 1.5

    Fault Tolerance In Mapreduce: Resilience To Node And Task Failures

    This section discusses the mechanisms implemented in MapReduce to ensure fault tolerance, focusing on task re-execution, data durability, and the overall resilience of the system.

    Learning Practice

  • 1.5.1

    Task Re-Execution

    Task re-execution in MapReduce ensures resilience and fault tolerance during the execution of Map and Reduce tasks.

    Learning Practice

  • 1.5.2

    Intermediate Data Durability (Mapper Output)

    This section discusses intermediate data durability in MapReduce, emphasizing the importance of maintaining the Mapper output for successful job execution.

    Learning Practice

  • 1.5.3

    Heartbeating And Failure Detection

    This section covers the heartbeating mechanism used in the Hadoop ecosystem for failure detection within the MapReduce framework.

    Learning Practice

  • 1.5.4

    Jobtracker/resourcemanager Fault Tolerance

    This section outlines how fault tolerance is managed within the JobTracker and ResourceManager components of the Hadoop ecosystem, focusing on both early and modern implementations.

    Learning

  • 1.5.5

    Speculative Execution

    Speculative Execution enhances MapReduce performance by reducing job completion times by launching duplicate tasks for slower tasks.

    Learning Practice

  • 1.6

    Implementation Overview (Apache Hadoop Mapreduce)

    This section provides an overview of Apache Hadoop MapReduce, detailing its programming model, phases of execution, and applications in distributed data processing.

    Learning Practice

  • 1.6.1

    Hdfs (Hadoop Distributed File System)

    This section focuses on HDFS, a foundational component of the Hadoop ecosystem, emphasizing its role in distributed data storage and fault tolerance.

    Learning Practice

  • 1.6.1.1

    Primary Storage

    This section explores the fundamental technologies of MapReduce, emphasizing its role in big data processing and distributed computing.

    Learning Practice

  • 1.6.1.2

    Fault-Tolerant Storage

    This section discusses the significance of fault-tolerant storage in distributed computing, specifically through Hadoop's HDFS and its role in MapReduce.

    Learning Practice

  • 1.6.1.3

    Data Locality

    This section discusses data locality in the context of distributed computing, focusing on the importance of executing tasks close to the data they operate on.

    Learning Practice

  • 1.6.2

    Yarn (Yet Another Resource Negotiator)

    YARN is a resource management layer for Hadoop that improves cluster resource management and job scheduling by decoupling these tasks from computational frameworks.

    Learning Practice

  • 1.7

    Examples Of Mapreduce Workflow (Detailed)

    This section provides detailed examples of MapReduce workflows, specifically focusing on common applications such as Word Count and Inverted Index.

    Learning Practice

  • 1.7.1

    Word Count

    Learning

  • 1.7.2

    Inverted Index

    This section provides an overview of the Inverted Index, detailing its construction and application in search engines and information retrieval systems.

    Learning Practice

  • 2

    Introduction To Spark: General-Purpose Cluster Computing

    Apache Spark is an advanced open-source analytics engine optimized for in-memory computation, overcoming the limitations of MapReduce and enabling a wider range of data processing tasks.

    Learning Practice

  • 2.1

    Resilient Distributed Datasets (Rdds): The Foundational Abstraction

    This section introduces Resilient Distributed Datasets (RDDs) as the core data abstraction in Apache Spark, emphasizing their characteristics and operations.

    Learning Practice

  • 2.1.1

    Resilient (Fault-Tolerant)

    This section examines the concepts of fault tolerance and resilience in modern data processing frameworks like MapReduce and Spark, highlighting their importance for ensuring uninterrupted data workflows.

    Learning Practice

  • 2.1.2

    Distributed

    This section explores distributed data processing technologies, specifically focusing on MapReduce, Spark, and Apache Kafka widely used in big data applications.

    Learning Practice

  • 2.1.3

    Datasets

    This section discusses the fundamental technologies for processing and managing large datasets and real-time data streams in cloud environments, focusing on MapReduce, Spark, and Apache Kafka.

    Learning Practice

  • 2.1.4

    Lazy Evaluation

    Lazy evaluation in Spark optimizes performance by delaying execution until necessary.

    Learning Practice

  • 2.2

    Rdd Operations: Transformations And Actions

    This section covers RDD operations in Apache Spark, highlighting the differences between transformations and actions, and their significance in data processing.

    Learning Practice

  • 2.2.1

    Transformations (Lazy Execution)

    This section highlights the concept of transformations in Apache Spark, emphasizing the mechanism of lazy execution, which allows for optimization by delaying computation until necessary.

    Learning Practice

  • 2.2.2

    Actions (Eager Execution)

    This section focuses on Apache Spark's actions, which are eager executions that trigger the computation of transformations applied to Resilient Distributed Datasets (RDDs).

    Learning Practice

  • 2.3

    Spark Applications: A Unified Ecosystem For Diverse Workloads

    This section outlines Apache Spark's capabilities as a unified platform for various big data workloads, highlighting its libraries for SQL, streaming, machine learning, and graph processing.

    Learning Practice

  • 2.3.1

    Spark Sql

    This section highlights how Spark SQL enhances data processing with structured data and SQL interfaces within big data ecosystems.

    Learning Practice

  • 2.3.2

    Spark Streaming (Dstreams)

    This section explores Spark Streaming and its discrete streaming capability using DStreams for real-time data processing and analytics.

    Learning Practice

  • 2.3.3

    Mllib (Machine Learning Library)

    MLlib provides scalable machine learning algorithms for big data processing using Apache Spark.

    Learning Practice

  • 2.3.4

    Graphx

    This section introduces GraphX, a powerful Spark library designed for graph-parallel computation, discussing its structure, functionality, and real-world applications.

    Learning Practice

  • 2.4

    Pagerank Algorithm With Spark (Illustrative Example)

    The PageRank algorithm efficiently ranks web pages using Spark's in-memory processing power, leveraging RDDs for improved performance in iterative calculations.

    Learning Practice

  • 2.4.1

    Core Idea

    This section introduces core technologies for large-scale distributed data processing, focusing on MapReduce and its evolution into Apache Spark, along with Apache Kafka's role in real-time data processing.

    Learning Practice

  • 2.4.2

    Algorithm Steps (Iterative)

    This section explores the algorithm steps involved in iterative processing with a focus on Apache Spark and its capabilities for handling iterative data workflows.

    Learning Practice

  • 2.4.3

    Spark Rdd-Based Implementation

    This section covers the fundamentals of Spark's Resilient Distributed Dataset (RDD) model, its operations, and its importance for efficient data processing.

    Learning Practice

  • 2.5

    Graphx: Graph-Parallel Computation In Spark

    GraphX is a Spark component designed for efficient graph-parallel computation, integrating various graph processing capabilities with Spark's data processing framework.

    Learning Practice

  • 2.5.1

    Property Graph Model

    The Property Graph Model in GraphX facilitates graph-parallel computation with vertices and edges having user-defined properties.

    Learning Practice

  • 2.5.2

    Graphx Api: Combining Flexibility And Efficiency

    The GraphX API in Apache Spark allows for efficient graph processing by combining the flexibility of RDD transformations with specialized graph algorithms.

    Learning Practice

  • 2.5.2.1

    Graph Operators

    This section discusses graph operators in Apache Spark, focusing on high-level operations that manipulate the structure and data of graphs.

    Learning Practice

  • 2.5.2.2

    Pregel Api (Vertex-Centric Computation)

    The Pregel API in Apache Spark facilitates vertex-centric computation for graph algorithms, allowing for efficient iterative processing through message-passing and superstep operations.

    Learning Practice

  • 2.5.2.2.1

    Supersteps

    This section introduces the Pregel computation model used in graph processing, highlighting the concept of supersteps and the interactions of vertex state and message passing.

    Learning Practice

  • 2.5.2.2.2

    Vertex State

    This section introduces the concept of vertex state in graph processing using Apache Spark's GraphX library.

    Learning Practice

  • 2.5.2.2.3

    Message Passing

    This section explores the essential concepts of message passing in distributed systems, focusing on the MapReduce, Spark, and Kafka frameworks.

    Learning Practice

  • 2.5.2.2.4

    Activation

    This section discusses the critical roles of MapReduce, Spark, and Apache Kafka in cloud applications for processing large datasets and real-time data streams.

    Learning Practice

  • 2.5.2.2.5

    Termination

    This section provides a comprehensive overview of the implementation and significance of termination within advanced cloud-oriented frameworks such as MapReduce, Spark, and Kafka.

    Learning Practice

  • 2.5.3

    Graphx Working (High-Level Data Flow)

    This section describes the high-level data flow in GraphX, focusing on graph construction, optimized representation, and execution using the Pregel API.

    Learning Practice

  • 2.5.3.1

    Graph Construction

    This section covers the essential concepts of graph construction within Apache Spark, including the foundational abstractions, the use of VertexRDD and EdgeRDD, and optimizing data representation in graph computations.

    Learning Practice

  • 2.5.3.2

    Optimized Graph Representation

    This section discusses the optimized representation of graphs in the context of distributed computing frameworks like Apache Spark's GraphX.

    Learning Practice

  • 2.5.3.3

    Execution With Pregel

    This section discusses the Pregel API in GraphX for iterative graph algorithms, highlighting its design for efficient vertex-centric computations.

    Learning Practice

  • 2.5.3.4

    Integration With Spark Core

    This section discusses how Apache Spark integrates core functionalities for distributed data processing, enhancing capabilities over traditional MapReduce.

    Learning Practice

  • 3

    Introduction To Kafka: Distributed Streaming Platform

    This section introduces Apache Kafka, a distributed streaming platform that enables real-time data pipelines, emphasizing its architecture, key features, and use cases.

    Learning Practice

  • 3.1

    What Is Kafka? More Than Just A Message Queue

    Kafka is a distributed streaming platform that enables real-time data pipelines and stream processing, characterized by its durability, high throughput, low latency, and fault tolerance.

    Learning

  • 3.1.1

    Distributed

    This section explores the foundational concepts and technologies of distributed data processing, focusing on MapReduce, Spark, and Kafka.

    Learning Practice

  • 3.1.2

    Publish-Subscribe Model

    The Publish-Subscribe model is a messaging pattern that decouples message producers and consumers, facilitating efficient real-time data transfer and event-driven architectures.

    Learning Practice

  • 3.1.3

    Persistent & Immutable Log

    This section explores the concept of a persistent and immutable log in the context of Apache Kafka, highlighting its features and significance in modern data streaming applications.

    Learning Practice

  • 3.1.4

    High Throughput

    This section discusses the importance of high throughput in modern cloud applications, focusing on key technologies like MapReduce, Spark, and Apache Kafka.

    Learning Practice

  • 3.1.5

    Low Latency

    This section discusses low latency in cloud applications, focusing on technologies like MapReduce, Spark, and Apache Kafka that enable efficient processing of vast datasets and real-time data streams.

    Learning Practice

  • 3.1.6

    Fault-Tolerant

    The section covers essential concepts of fault tolerance in distributed systems, particularly in MapReduce, including mechanisms like task re-execution, intermediate data durability, and heartbeat monitoring.

    Learning Practice

  • 3.1.7

    Scalable

    This section provides an in-depth look at key technologies for distributed data processing, specifically MapReduce, Apache Spark, and Apache Kafka.

    Learning Practice

  • 3.2

    Use Cases For Kafka: Driving Modern Data Architectures

    Kafka acts as a cornerstone for modern cloud applications by enabling real-time data pipelines, streaming analytics, and event-driven microservices.

    Learning Practice

  • 3.2.1

    Real-Time Data Pipelines (Etl)

    This section explores the core technologies of MapReduce, Spark, and Kafka that facilitate real-time data processing and robust data pipelines in cloud environments.

    Learning Practice

  • 3.2.2

    Streaming Analytics

    This section explores the technologies involved in streaming analytics, focusing on real-time data processing using Apache Kafka.

    Learning Practice

  • 3.2.3

    Event Sourcing

    Event Sourcing is a software architectural pattern that revolves around storing the state of an application as a sequence of events, enabling better traceability and scalability.

    Learning Practice

  • 3.2.4

    Log Aggregation

    Log aggregation is critical for centralizing log data from distributed systems, enabling comprehensive monitoring and analysis.

    Learning Practice

  • 3.2.5

    Metrics Collection

    This section discusses metrics collection as a vital component of modern cloud applications, highlighting its significance in analyzing and optimizing performance.

    Learning Practice

  • 3.2.6

    Decoupling Microservices

    This section discusses the significance and mechanics of decoupling microservices using Kafka, a powerful tool for developing robust, scalable, and event-driven applications.

    Learning Practice

  • 3.3

    Data Model: Topics, Partitions, And Offsets

    The section describes the core data model of Apache Kafka, focusing on topics, partitions, and message offsets.

    Learning Practice

  • 3.3.1

    Topic

    Learning

  • 3.3.2

    Partition

    This section covers core technologies for processing vast datasets and real-time streams, focusing on MapReduce, Spark, and Kafka.

    Learning Practice

  • 3.3.3

    Broker (Kafka Server)

    This section introduces the Kafka broker, which is essential for managing distributed messaging in Kafka.

    Learning Practice

  • 3.4

    Architecture Of Kafka: A Decentralized And Replicated Log

    Kafka's architecture provides a distributed, high-performance system for handling real-time data streams through its decentralized log structure.

    Learning Practice

  • 3.4.1

    Kafka Cluster

    This section introduces Apache Kafka as a distributed streaming platform crucial for handling large-scale real-time data processing.

    Learning Practice

  • 3.4.2

    Zookeeper (For Coordination)

    This section introduces Apache ZooKeeper, highlighting its role in managing distributed systems for coordination and synchronization.

    Learning Practice

  • 3.4.2.1

    Broker Registration

    This section explores how brokers register in a Kafka cluster, which is essential for managing topics and partitions.

    Learning Practice

  • 3.4.2.2

    Topic/partition Metadata

    Learning

  • 3.4.2.3

    Controller Election

    This section discusses the controller election process that ensures reliable operation and fault tolerance in Kafka's distributed systems.

    Learning Practice

  • 3.4.2.4

    Consumer Group Offsets (In Older Versions)

    This section discusses how consumer offsets were managed in older versions of Kafka, emphasizing the role of ZooKeeper in tracking offset data for consumer groups.

    Learning Practice

  • 3.4.2.5

    Failure Detection

    This section covers the mechanisms used by MapReduce for detecting and recovering from task failures, ensuring robustness in large-scale data processing.

    Learning Practice

  • 3.5

    Producers

    This section discusses the essential role of producers in various cloud application technologies.

    Learning Practice

  • 3.6

    Consumers And Consumer Groups

    The chapter explores the structure and functionality of consumers and consumer groups within Apache Kafka, focusing on their role in the processing of streaming data.

    Learning Practice

  • 3.7

    Partition Leaders And Followers (Replication)

    This section explores the roles and responsibilities of partition leaders and followers in Kafka's architecture, highlighting their importance in data replication and fault tolerance.

    Learning Practice

  • 3.8

    Types Of Messaging Systems: Kafka's Evolution And Distinction

    This section discusses the evolution of messaging systems, with a focus on Apache Kafka's design and functionality as a hybrid messaging and streaming platform.

    Learning Practice

  • 3.8.1

    Traditional Message Queues (E.g., Rabbitmq, Activemq, Ibm Mq)

    This section explores traditional message queue systems like RabbitMQ, ActiveMQ, and IBM MQ, highlighting their features, differences from Kafka, and common use cases.

    Learning Practice

  • 3.8.2

    Enterprise Messaging Systems

    This section provides an overview of enterprise messaging systems, focusing on Apache Kafka as a key technology for building scalable and fault-tolerant data pipelines.

    Learning Practice

  • 3.8.3

    Distributed Log Systems (E.g., Apache Bookkeeper, Hdfs Append-Only Files)

    This section covers the fundamental aspects of distributed log systems, focusing on their architecture, use cases, and the significance of retaining immutability in data storage.

    Learning Practice

  • 3.8.4

    Kafka's Hybrid Nature

    Apache Kafka integrates features from traditional messaging systems and distributed logs, enabling high throughput and low latency for real-time data streams.

    Learning Practice

  • 3.9

    Importance Of Brokers In Kafka: The Backbone Of The Cluster

    Kafka brokers are vital servers in the Kafka ecosystem, handling data storage, message management, and ensuring fault tolerance of the Kafka system.

    Learning Practice

References

Untitled document (26).pdf

Class Notes

Memorization

What we have learnt

  • MapReduce is a programming ...
  • Apache Spark extends the ca...
  • Apache Kafka serves as a di...

Final Test

Revision Tests