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    • Apache Spark with Scala Useful for Databricks Certification

    Apache Spark with Scala useful for Databricks Certification

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    Udemy

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    Lavel

    Beginner

    Mode

    Online

    Fees

    ₹ 449 2299

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    Fees information
    ₹ 449  ₹2,299
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    Introduction

    Download Resources

    Introduction to Spark and Spark Architecture Components

    • Introduction to Spark
    • (Old) Free Account creation in Databricks
    • (New) Free Account creation in Databricks
    • Provisioning a Spark Cluster
    • Basics about Notebooks
    • Why we should learn Apache Spark?
    • Spark Architecture Components
    • Driver
    • Partitions
    • Executors

    Spark Execution

    • Spark Jobs
    • Spark Stages
    • Spark Tasks
    • Practical Demonstration of Jobs, Tasks and Stages

    Spark SQL, Dataframes and Datasets

    • Spark RDD (Create and Display Practical)
    • Spark Dataframe (Create and Display Practical)
    • Anonymus Functions in Scala
    • Extra (Optional on Spark DataFrame)
    • Extra (Optional on Spark DataFrame) in Details
    • Spark Datasets (Create and Display Practical)
    • Caching
    • Notes on reading files with Spark
    • Data Source CSV File
    • Data Source JSON File
    • Data Source LIBSVM File
    • Data Source Image File
    • Data Source Arvo File
    • Data Source Parquet File
    • Untyped Dataset Operations (aka DataFrame Operations)
    • Running SQL Queries Programmatically
    • Global Temporary View
    • Creating Datasets
    • Scalar Functions (Built-in Scalar Functions) Part 1
    • Scalar Functions (Built-in Scalar Functions) Part 2
    • Scalar Functions (Built-in Scalar Functions) Part 3
    • User Defined Scalar Functions

    Spark RDD

    • Operation in Apache Spark
    • Transformations
    • map(function)
    • filter(function)
    • flatMap(function)
    • mapPartitions(func)
    • mapPartitionsWithIndex(func)
    • sample(withReplacement, fraction, seed)
    • union(otherDataset)
    • intersection(otherDataset)
    • distinct([numPartitions]))
    • groupby(func)
    • groupByKey([numPartitions])
    • reduceByKey(func, [numPartitions])
    • aggregateByKey(zeroValue)(seqOp, combOp, [numPartitions])
    • sortByKey([ascending], [numPartitions])
    • join(otherDataset, [numPartitions])
    • cogroup(otherDataset, [numPartitions])
    • cartesian(otherDataset)
    • coalesce(numPartitions)
    • repartition(numPartitions)
    • repartitionAndSortWithinPartitions(partitioner)
    • Wide vs. Narrow Transformations
    • Actions
    • reduce(func)
    • collect()
    • count()
    • first()
    • take(n)
    • takeSample(withReplacement, num, [seed])
    • takeOrdered(n, [ordering])
    • countByKey()
    • foreach(func)
    • Shuffling
    • Persistence (Cache)
    • Unpersist
    • Broadcast Variables
    • Accumulators
    • Important Lecture
    • Bonus

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