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    Learning PySpark

    BY
    Udemy

    Gain a thorough understanding of the strategies and methodologies used with Python and Apache Spark to develop and deploy applications.

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    Mode

    Online

    Fees

    ₹ 499 1799

    Quick Facts

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

    Course overview

    Python's PySpark provides an interface for Apache Spark. Along with offering the PySpark shell for collaborative data analysis in a distributed environment, PySpark allows users to create Spark applications using Python APIs. The majority of Spark's features, including Spark SQL, DataFrame, Streaming, MLlib, and Spark Core, are supported by PySpark. Learning PySpark online certification is designed by Packt Publishing - a learning platform that is intended to offer certifications to help applicants improve their knowledge and is delivered by Udemy.

    Learning PySpark online training is a short-term program that contains 2.5 hours of video lectures along with downloadable resources which begins by offering applicants a solid understanding of Apache Spark and how to set up a Python environment for Spark. With the help of Learning PySpark online classes, applicants will learn about Spark SQL, Spark DataFrame, API, lazy execution, data sorting, data aggregation, and data transformation as well as will learn how to process data using Spark DataFrames and master data collection methods by distributed data processing.

    The highlights

    • Certificate of completion
    • Self-paced course
    • 2.5 hours of pre-recorded video content
    • 1 downloadable resource

    Program offerings

    • Online course
    • Learning resources
    • 30-day money-back guarantee
    • Unlimited access
    • Accessible on mobile devices and tv

    Course and certificate fees

    Fees information
    ₹ 499  ₹1,799
    certificate availability

    Yes

    certificate providing authority

    Udemy

    What you will learn

    Knowledge of apache spark

    After completing the Learning PySpark certification course, applicants will gain a solid understanding of the fundamentals of PySpark as well as will acquire the knowledge of the functionalities of Apache Spark and Spark 2.0 for application development and deployment. In this PySpark course, applicants will explore the functionalities of Spark SQL, Spark Dataframes, API, and Dataframes as well as will acquire an understanding of the schemas for transformations, lazy execution, and resilient distributed datasets. In this PySpark certification, applicants will also learn about the methodologies involved with data aggregation, data transformation, and data sorting.

    The syllabus

    A Brief Primer on PySpark

    • The Course Overview
    • Brief Introduction to Spark
    • Apache Spark Stack
    • Spark Execution Process
    • Newest Capabilities of PySpark 2.0+
    • Cloning GitHub Repository

    Resilient Distributed Datasets

    • Brief Introduction to RDDs
    • Creating RDDs
    • Schema of an RDD
    • Understanding Lazy Execution
    • Introducing Transformations – .map(…)
    • Introducing Transformations – .filter(…)
    • Introducing Transformations – .flatMap(…)
    • Introducing Transformations – .distinct(…)
    • Introducing Transformations – .sample(…)
    • Introducing Transformations – .join(…)
    • Introducing Transformations – .repartition(…)

    Resilient Distributed Datasets and Actions

    • Introducing Actions – .take(…)
    • Introducing Actions – .collect(…)
    • Introducing Actions – .reduce(…) and .reduceByKey(…)
    • Introducing Actions – .count()
    • Introducing Actions – .foreach(…)
    • Introducing Actions – .aggregate(…) and .aggregateByKey(…)
    • Introducing Actions – .coalesce(…)
    • Introducing Actions – .combineByKey(…)
    • Introducing Actions – .histogram(…)
    • Introducing Actions – .sortBy(…)
    • Introducing Actions – Saving Data
    • Introducing Actions – Descriptive Statistics

    DataFrames and Transformations

    • Introduction
    • Creating DataFrames
    • Specifying Schema of a DataFrame
    • Interacting with DataFrames
    • The .agg(…) Transformation
    • The .sql(…) Transformation
    • Creating Temporary Tables
    • Joining Two DataFrames
    • Performing Statistical Transformations
    • The .distinct(…) Transformation

    Data Processing with Spark DataFrames

    • Schema Changes
    • Filtering Data
    • Aggregating Data
    • Selecting Data
    • Transforming Data
    • Presenting Data
    • Sorting DataFrames
    • Saving DataFrames
    • Pitfalls of UDFs
    • Repartitioning Data

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