13. Big Data Technologies (Hadoop, Spark)

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Navigate through the learning materials and practice exercises.

1. 13

   Big Data Technologies (Hadoop, Spark)

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   This section introduces the fundamental big data technologies, Apache Hadoop...

2. 13.1

   Understanding Big Data

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   Big Data encompasses massive, complex datasets that require advanced tools...

3. 13.1.1

   What Is Big Data?

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   Big Data refers to extremely large and complex datasets that traditional...

4. 13.1.2

   Challenges In Big Data Processing

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   This section outlines the key challenges faced in big data processing,...

5. 13.2

   Apache Hadoop

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   Apache Hadoop is an open-source framework designed for distributed storage...

6. 13.2.1

   What Is Hadoop?

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   Apache Hadoop is an open-source framework designed for distributed storage...

7. 13.2.2

   Core Components Of Hadoop

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   This section covers the core components of Apache Hadoop, detailing HDFS,...

8. 13.2.2.1

   Hdfs (Hadoop Distributed File System)

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   HDFS is a distributed storage system that underpins Apache Hadoop, enabling...

9. 13.2.2.2

   Mapreduce

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   MapReduce is a programming model in Hadoop for processing large data sets...

10. 13.2.2.3

    Yarn (Yet Another Resource Negotiator)

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    YARN is a crucial component of Apache Hadoop that manages cluster resources...

11. 13.2.3

    Hadoop Ecosystem

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    The Hadoop Ecosystem consists of various tools designed to enhance data...

12. 13.2.4

    Advantages Of Hadoop

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    Hadoop offers effective solutions for big data management through...

13. 13.2.5

    Limitations Of Hadoop

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    This section outlines the key limitations of Hadoop, including its high...

14. 13.3

    Apache Spark

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    Apache Spark is a fast, in-memory distributed computing framework that...

15. 13.3.1

    What Is Apache Spark?

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    Apache Spark is a fast, in-memory distributed computing framework designed...

16. 13.3.2

    Spark Core Components

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    The Spark Core Components section outlines the fundamental building blocks...

17. 13.3.2.1

    Spark Core

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    This section introduces Spark Core, the fundamental execution engine of...

18. 13.3.2.2

    Spark Sql

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    Spark SQL is a component of Apache Spark, designed for processing structured...

19. 13.3.2.3

    Spark Streaming

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    Spark Streaming enables real-time data processing within the Apache Spark...

20. 13.3.2.4

    Mllib (Machine Learning Library)

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    MLlib is Spark's integrated machine learning library that offers a variety...

21. 13.3.2.5

    Graphx

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    GraphX is a Spark API that facilitates graph computations and analysis,...

22. 13.3.3

    Rdds And Dataframes

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    This section introduces RDDs and DataFrames, two fundamental data structures...

23. 13.3.4

    Spark Execution Model

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    The Spark Execution Model describes how Apache Spark processes data through...

24. 13.3.5

    Advantages Of Spark

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    This section outlines the key advantages of Apache Spark, highlighting its...

25. 13.3.6

    Limitations Of Spark

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    The limitations of Apache Spark primarily revolve around its memory...

26. 13.4

    Hadoop Vs. Spark

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    This section compares Hadoop and Spark, highlighting their respective...

27. 13.5

    Integration And Use Cases

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    This section discusses when to use Hadoop and Spark, including their...

28. 13.5.1

    When To Use Hadoop?

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    Hadoop is best utilized for cost-sensitive, large-scale batch processing and...

29. 13.5.2

    When To Use Spark?

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    This section outlines the scenarios in which Apache Spark is the preferred...

30. 13.5.3

    Using Hadoop And Spark Together

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    This section explores how Apache Hadoop and Apache Spark can be integrated...

31. 13.6

    Real-World Applications

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    This section explores the various real-world applications of big data...