PySpark Development Course Overview

Sign up for MeeshaSoftware's PySpark Development Introduction Course, which lasts for one day. If you are familiar with machine learning, then this PySpark Development Course will teach you how to build and implement data-intensive applications by utilizing Spark RDD, Spark SQL, Spark MLlib, Spark Streaming, HDFS, Flume, Spark GraphX, and Kafka. The course also assumes that you are familiar with machine learning. It assists you with gaining the knowledge and abilities necessary to become a PySpark developer.

Target Audience:

Big Data developers and architects, BI/ETL/DW professionals, mainframe professionals, Big Data architects, engineers, developers, and data scientists and analytics experts are the target audience for this PySpark Development Course. Mainframe professionals are also welcome to enroll.

Due to the limited availability of this program, it may take up to three weeks to organize the necessary logistics.

You are going to learn:

 Module 1:A Quick Introduction to PySpark is Covered in Module 1

• A Concise Introduction to PySpark
• A Concise Introduction to the Program Spark
• The Apache Spark Platform
• Process of Execution in Spark
• PySpark's Most Recent Capabilities and Updates
• The process of cloning a GitHub repository

 Module 2: The "Resilient Distributed Datasets" Module is the Second One.

• Making Use Of RDDs
• The structure of an RDD
• Acquiring Knowledge about Lazy Execution
• A Brief Introduction to Transformations Using the. map(...)
• The First Step Towards Transformations:.filter(...)
• A Brief Introduction to Transformations Using the.flatMap(...) Function
• A Brief Introduction to Transformations Using the. distinct(...) Function
• A Brief Introduction to Transformations Using. sample(...)
• An Introduction to Transformations Using the. join(...) Operator
• A Brief Introduction to Transformations:.repartition(...)

Module 3: Highly Available Distributed Datasets and Operations

• An Introduction to Actions Using the.collect(...) Keyword
• We are now introducing two new Actions:.reduce(...) and.reduceByKey(...)
• Introducing Actions with the Dot-Count Extension ()
• A Brief Introduction to Actions.foreach(...)
• The.aggregate(...) and.aggregateByKey(...) Actions Are Now Available.
• Coalesce is the first action that will be introduced.
• Actions are now going to be introduced.
• combineByKey(…)
• Hitting the Ground Running with Actions – histogram(...)
• A Brief Introduction to Actions:.sortBy(...)
• Actions are being introduced, and we will be saving data.
• Descriptive Statistics to Begin the Presentation of Actions

 Module 4: The DataFrames and Transformations Module is the fourth in the series.

• Developing Dataframes for Analysis

• Specifying the DataFrame's Internal Schema

• Using DataFrames in Your Interactions

• The Transformation of the.agg(...) Extension

• The Transformation Using.sql(...)

• Putting Together Distraction-Free Tables

• Putting Together Two Data Frames

• Putting Statistical Transformations Into Practice

• The Transformation Using the Dot Dot Dot Notation

Module 5: Processing of Data Using Spark DataFrames is Covered.

• Filtering Data
• Aggregating Data
• Choosing Among the Data
• Transforming Data
• Presenting Data
• DataFrames are being sorted
• Saving DataFrames
• Challenges posed by UDFs
• Adjusting the Data Partitioning