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Apache Spark – PySpark

سرفصل های دوره

PySpark


1. THE FUNDAMENTALS
  • 1. Data VS Information
  • 2. Data Storage and Processing
  • 3. Data Sources
  • 4. Big Data Introduction

  • 2. THE FOUNDATIONS OF BIG DATA
  • 1. Emergence of Big Data
  • 2. Basic Terminologies
  • 3. Central theme of Big Data
  • 4. Requirements of Programming Model
  • 5. Understand Distributed Processing through a Story

  • 3. ENVIRONMENT AND INSTALLATION
  • 1. Oracle VirtualMachine_Installation
  • 2. How to install Ubuntu operating system on Virtual Box
  • 3. How to install PySpark on Ubuntu with Java and Python 3
  • 4. How to configure Pyspark with Pycharm with_Installation
  • 5. Google Cloud Platform Setup

  • 4. HADOOP ECOSYSTEM
  • 1. Introduction to Hadoop Ecosystem

  • 5. PYTHON FOR PYSPARK
  • 1. INTRODUCTION TO PROGRAMMING
  • 2. Introduction to Python
  • 3. Environment for Python
  • 4. Executing Python Code
  • 5. Syntax, Indentation and Comments
  • 6. Syntax, Indentation and Comments - Practical
  • 7. Variables
  • 8. Variable Practicals
  • 9. Python Datatypes
  • 10. Python Datatypes Practicals
  • 11. Python Operator Concepts
  • 12. Python Operator Practicals
  • 13. Control Flows in Python
  • 14. Control Flows - IF ELSE Concepts
  • 15. If Else Practical
  • 16. Loops Theory
  • 17. Loops Practical
  • 18. Python Function Concepts
  • 19. Python Function Hands-on

  • 6. APACHE SPARK
  • 1. Why Spark
  • 2. Advantages of Spark
  • 3. What is Spark
  • 4. Components of Spark
  • 5. History of Spark

  • 7. OVERVIEW OF SPARK
  • 1. Architecture of Spark
  • 2. Spark Session
  • 3. Spark Session Terminal And Jupyter notebook Hands-On
  • 4. Spark Language API
  • 5. Dataframes and Partitions
  • 6. Spark Transformations
  • 7. Spark Actions

  • 8. STRUCTURED API OVERVIEW
  • 1. Structured APIs - Dataframes and Datasets
  • 2. Schema Definition
  • 3. Spark Types
  • 4. Structured API Execution

  • 9. OPERATIONS ON DATAFRAMES
  • 1. Dataframe Columns
  • 2. Columns as Expression
  • 3. Dataframe Rows
  • 4. Ways of Creating Dataframe
  • 5. Methods to Manipulate Columns
  • 6. DataFrame Transformations
  • 7. Dataframe Transformation - Columns
  • 8. Dataframe Transformations - Rows Part1
  • 9. Dataframe Transformation - Rows Part2

  • 10. WORKING WITH DIFFERENT TYPES OF DATABASE
  • 1. Introduction to working with Different Types of Data
  • 2. Working with Booleans
  • 3. Working with Strings
  • 4. Working with Strings Practical1
  • 5. Working with Strings Practical2
  • 6. Working with Date and Time Stamps
  • 7. Working with Null Concepts
  • 8. Working with Nulls Practicals
  • 9. Working with Complex Types
  • 10. Working with Complex types practical
  • 11. User Defined Functions - Concepts
  • 12. Working with Complex types practical

  • 11. CREATING DATAFRAMES FROM DIFFERENT SOURCES
  • 1. Data Sources Introduction
  • 2. Read-API- Data Sources
  • 3. Read-API-Practical
  • 4. Write-API-Data Sources
  • 5. Write-API-Practical
  • 6. Reading from CSV Files
  • 7. Writing into CSV Files
  • 8. Reading from JSON Files and Writing into JSON
  • 9. Reading from Parquet and writing into Parquet
  • 10. Reading from ORC and writing into ORC
  • 11. Unstructured Data - Text File - Reading and Writing
  • 12. Introduction to reading data from structured sources
  • 13. Reading data from structured sources - Database - Concepts
  • 14. Reading data from structured sources - Database - Practicals
  • 15. Query Pushdown Concepts
  • 16. Query Pushdown Praticals
  • 17. Writing into structured sources - Database - Concepts
  • 18. Writing into structured sources - Database - Practicals

  • 12. AGGREGATIONS
  • 1. Introduction to Aggregations
  • 2. Aggregataion Concepts - Count
  • 3. Aggregation Practical-1-Count
  • 4. Aggregation Concepts - First, Sum and Average
  • 5. Aggregation - Practical 2 - First Last Average
  • 6. Aggregation-Practical-3-StatisticalFunctions
  • 7. Aggregation Concepts - Grouping
  • 8. Aggregation-Practical-4-GroupBy
  • 9. Aggregation Concepts - Window Functions
  • 10. Aggregation-Practical-5-WindowFunctions
  • 11. Aggregation Concepts - RollUp and Cube
  • 12. Aggregation-Practical-6-RollupandCube

  • 13. SPARK JOINS
  • 1. Spark Joins Theory-1-Introduction
  • 2. Spark Joins Theory-2-How Joins Work
  • 3. Spark Joins-Theory-3-Inner Joins
  • 4. Spark Joins -Practical -1-Innerjoins
  • 5. Saprk Joins - Theory-4 - Outer Joins
  • 6. Spark Joins -Practical - Outer Joins
  • 7. Spark Joins -Theory - 5-Left Semi And Anti Joins
  • 8. Spark Joins - Practical - Left Semi And Anti Joins
  • 9. Spark Joins -Theory -6-CrossJoin
  • 10. Spark Joins - Practical- Cross Joins
  • 11. Spark Joins -Theory -7-Challenges In Joins
  • 12. Spark Joins-5-Practical-Tackling the Challenges in Joins
  • 13. Spark Joins -Theory -8-Communication Strategies

  • 14. RESILIENT DISTRIBUTED DATASETS- RDDs
  • 1. What is an RDD
  • 2. Introduction to Low Level APIs
  • 3. Properties Of RDD
  • 4. When to use RDDs
  • 5. Creating RDDs
  • 6. RDD Practical-1-Creating RDDs
  • 7. RDD Lineage
  • 8. RDD Transformations
  • 9. RDD - Transformations Practical
  • 10. RDD Actions
  • 11. RDD Actions - Practical
  • 12. RDDT Saving To File
  • 13. RDD Saving to a File - Practical

  • 15. DISTRIBUTED VARIABLES
  • 1. Distributed Variables - Introduction
  • 2. Broadcast Variables
  • 3. Broadcast Variables - Practical
  • 4. Accumulators
  • 5. Accumulators - Practical

  • 16. HOW SPARK WORKS ON A CLUSTER
  • 1. Introduction
  • 2. How Spark runs on a Cluster - Cluster Manager
  • 3. How Spark runs on a Cluster - Execution Modes
  • 4. Life Cycle a Spark Application - Outside Spark
  • 5. Life Cycle of a Spark Application - Inside Spark
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    تاریخ انتشار: ۲۵ تیر ۱۴۰۲
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