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    Spark SQL and Spark 3 using Scala (Formerly CCA175)

    BY
    Udemy

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    Online

    Fees

    ₹ 3299

    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
    ₹ 3,299
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    Introduction

    • CCA 175 Spark and Hadoop Developer - Curriculum

    Setting up Environment using AWS Cloud9

    • Getting Started with Cloud9
    • Creating Cloud9 Environment
    • Warming up with Cloud9 IDE
    • Overview of EC2 related to Cloud9
    • Opening ports for Cloud9 Instance
    • Associating Elastic IPs to Cloud9 Instance
    • Increase EBS Volume Size of Cloud9 Instance
    • Setup Jupyter Lab on Cloud9
    • [Commands] Setup Jupyter Lab on Cloud9

    Setting up Environment - Overview of GCP and Provision Ubuntu VM

    • Signing up for GCP
    • Overview of GCP Web Console
    • Overview of GCP Pricing
    • Provision Ubuntu VM from GCP
    • Setup Docker
    • Why we are setting up Python and Jupyter Lab for Scala related course?
    • Validating Python
    • Setup Jupyter Lab

    Setup Hadoop on Single Node Cluster

    • Introduction to Single Node Hadoop Cluster
    • Setup Prerequisties
    • [Commands] - Setup Prerequisites
    • Setup Password less login
    • [Commands] - Setup Password less login
    • Download and Install Hadoop
    • [Commands] - Download and Install Hadoop
    • Configure Hadoop HDFS
    • [Commands] - Configure Hadoop HDFS
    • Start and Validate HDFS
    • [Commands] - Start and Validate HDFS
    • Configure Hadoop YARN
    • [Commands] - Configure Hadoop YARN
    • Start and Validate YARN
    • [Commands] - Start and Validate YARN
    • Managing Single Node Hadoop
    • [Commands] - Managing Single Node Hadoop

    Setup Hive and Spark on Single Node Cluster

    • Setup Data Sets for Practice
    • [Commands] - Setup Data Sets for Practice
    • Download and Install Hive
    • [Commands] - Download and Install Hive
    • Setup Database for Hive Metastore
    • [Commands] - Setup Database for Hive Metastore
    • Configure and Setup Hive Metastore
    • [Commands] - Configure and Setup Hive Metastore
    • Launch and Validate Hive
    • [Commands] - Launch and Validate Hive
    • Scripts to Manage Single Node Cluster
    • [Commands] - Scripts to Manage Single Node Cluster
    • Download and Install Spark 2
    • [Commands] - Download and Install Spark 2
    • Configure Spark 2
    • [Commands] - Configure Spark 2
    • Validate Spark 2 using CLIs
    • [Commands] - Validate Spark 2 using CLIs
    • Validate Jupyter Lab Setup
    • [Commands] - Validate Jupyter Lab Setup
    • Intergrate Spark 2 with Jupyter Lab
    • [Commands] - Intergrate Spark 2 with Jupyter Lab
    • Download and Install Spark 3
    • [Commands] - Download and Install Spark 3
    • Configure Spark 3
    • [Commands] - Configure Spark 3
    • Validate Spark 3 using CLIs
    • [Commands] - Validate Spark 3 using CLIs
    • Intergrate Spark 3 with Jupyter Lab
    • [Commands] - Intergrate Spark 3 with Jupyter Lab

    Scala Fundamentals

    • Introduction and Setting up of Scala
    • Setup Scala on Windows
    • Basic Programming Constructs
    • Functions
    • Object Oriented Concepts - Classes
    • Object Oriented Concepts - Objects
    • Object Oriented Concepts - Case Classes
    • Collections - Seq, Set and Map
    • Basic Map Reduce Operations
    • Setting up Data Sets for Basic I/O Operations
    • Basic I/O Operations and using Scala Collections APIs
    • Tuples
    • Development Cycle - Create Program File
    • Development Cycle - Compile source code to jar using SBT
    • Development Cycle - Setup SBT on Windows
    • Development Cycle - Compile changes and run jar with arguments
    • Development Cycle - Setup IntelliJ with Scala
    • Development Cycle - Develop Scala application using SBT in IntelliJ

    Overview of Hadoop HDFS Commands

    • Getting help or usage of HDFS Commands
    • Listing HDFS Files
    • Managing HDFS Directories
    • Copying files from local to HDFS
    • Copying files from HDFS to local
    • Getting File Metadata
    • Previewing Data in HDFS File
    • HDFS Block Size
    • HDFS Replication Factor
    • Getting HDFS Storage Usage
    • Using HDFS Stat Commands
    • HDFS File Permissions
    • Overriding Properties

    Apache Spark 2 using Scala - Data Processing - Overview

    • Introduction for the module
    • Starting Spark Context using spark-shell
    • Overview of Spark read APIs
    • Previewing Schema and Data using Spark APIs
    • Overview of Spark Data Frame APIs
    • Overview of Functions to Manipulate Data in Spark Data Frames
    • Overview of Spark Write APIs

    Apache Spark 2 using Scala - Processing Column Data using Pre-defined Functions

    • Introduction to Pre-defined Functions
    • Creating Spark Session Object in Notebook
    • Create Dummy Data Frames for Practice
    • Categories of Functions on Spark DAta Frame Columns
    • Using Spark Special Functions - col
    • Using Spark Special Functions - lit
    • Manipulating String Columns using Spark Functions - Case Conversion and Length
    • Manipulating String Columns using Spark Functions - substring
    • Manipulating String Columns using Spark Functions - split
    • Manipulating String Columns using Spark Functions - Concatenating Strings
    • Manipulating String Columns using Spark Functions - Padding Strings
    • Manipulating String Columns using Spark Functions - Trimming unwanted characters
    • Date and Time Functions in Spark - Overview
    • Date and Time Functions in Spark - Date Arithmetic
    • Date and Time Functions in Spark - Using trunc and date_trunc
    • Date and Time Functions in Spark - Using date_format and other functions
    • Date and Time Functions in Spark - dealing with unix timestamp
    • Pre-defined Functions in Spark - Conclusion

    Apache Spark 2 using Scala - Basic Transformations using Data Frame

    • Introduction to Basic Transformations using Data Frame APIs
    • Starting Spark Context
    • Overview of Filtering using Spark Data Frame APIs
    • Filtering Data from Spark Data Frames - Reading Data and Understanding Schema
    • Filtering Data from Spark Data Frames - Task 1 - Equal Operator
    • Filtering Data from Spark Data Frames - Task 2 - Comparison Operators
    • Filtering Data from Spark Data Frames - Task 3 - Boolean AND
    • Filtering Data from Spark Data Frames - Task 4 - IN Operator
    • Filtering Data from Spark Data Frames - Task 5 - Between and Like
    • Filtering Data from Spark Data Frames - Task 6 - Using functions in Filter
    • Overview of Aggregations using Spark Data Frame APIs
    • Overview of Sorting using Spark Data Frame APIs
    • Solution - Get Delayed Counts using Spark Data Frame APIs - Part 1
    • Solution - Get Delayed Counts using Spark Data Frame APIs - Part 2
    • Solution - Getting Delayed Counts By Date using Spark Data Frame APIs

    Apache Spark 2 using Scala - Joining Data Sets

    • Prepare and Validate Data Sets
    • Starting Spark Session or Spark Context
    • Analyze Data Sets for Joins using Spark Data Frame APIs
    • Eliminate Duplicate records from Data Frame using Spark Data Frame APIs
    • Recap of Basic Transformations using Spark Data Frame APIs
    • Joining Data Sets using Spark Data Frame APIs - Problem Statements
    • Overview of Joins using Spark Data Frame APIs
    • Inner Join using Spark Data Fr - Get number of flights departed from US airports
    • Inner Join using Spark Data Fram - Get number of flights departed from US States
    • Outer Join using Spark Data Frame APIs - Get Aiports - Never Used

    Apache Spark 2 using SQL - Getting Started

    • Getting Started with Spark SQL - Overview
    • Overview of Spark Documentation
    • Launching and using Spark SQL CLI
    • Overview of Spark SQL Properties
    • Running OS Commands using Spark SQL
    • Understanding Spark Metastore Warehouse Directory
    • Managing Spark Metastore Databases
    • Managing Spark Metastore Tables
    • Retrieve Metadata of Spark Metastore Tables
    • Role of Spark Metastore or Hive Metastore
    • Exercise - Getting Started with Spark SQL

    Apache Spark 2 using SQL - Basic Transformations

    • Basic Transformation using Spark SQL - Introduction
    • Spark SQL - Overview
    • Define Problem Statement for Basic Transformations using Spark SQL
    • Prepare or Create Tables using Spark SQL
    • Projecting or Selecting Data using Spark SQL
    • Filtering Data using Spark SQL
    • Joining Tables using Spark SQL - Inner
    • Joining Tables using Spark SQL - Outer
    • Aggregating Data using Spark SQL
    • Sorting Data using Spark SQL
    • Conclusion - Final Solution using Spark SQL

    Apache Spark 2 using SQL - Basic DDL and DML

    • Introduction to Basic DDL and DML using Spark SQL
    • Create Spark Metastore Tables using Spark SQL
    • Overview of Data Types for Spark Metastore Table Columns
    • Adding Comments to Spark Metastore Tables using Spark SQL
    • Loading Data Into Spark Metastore Tables using Spark SQL - Local
    • Loading Data Into Spark Metastore Tables using Spark SQL - HDFS
    • Loading Data into Spark Metastore Tables using Spark SQL - Append and Overwrite
    • Creating External Tables in Spark Metastore using Spark SQL
    • Managed Spark Metastore Tables vs External Spark Metastore Tables
    • Overview of Spark Metastore Table File Formats
    • Drop Spark Metastore Tables and Databases
    • Truncating Spark Metastore Tables
    • Exercise - Managed Spark Metastore Tables

    Apache Spark 2 using SQL - DML and Partitioning

    • Introduction to DML and Partitioning of Spark Metastore Tables using Spark SQL
    • Introduction to Partitioning of Spark Metastore Tables using Spark SQL
    • Creating Spark Metastore Tables using Parquet File Format
    • Load vs. Insert into Spark Metastore Tables using Spark SQL
    • Inserting Data using Stage Spark Metastore Table using Spark SQL
    • Creating Partitioned Spark Metastore Tables using Spark SQL
    • Adding Partitions to Spark Metastore Tables using Spark SQL
    • Loading Data into Partitioned Spark Metastore Tables using Spark SQL
    • Inserting Data into Partitions of Spark Metastore Tables using Spark SQL
    • Using Dynamic Partition Mode to insert data into Spark Metastore Tables
    • Exercise - Partitioned Spark Metastore Tables using Spark SQL

    Apache Spark 2 using SQL - Pre-defined Functions

    • Introduction - Overview of Spark SQL Functions
    • Overview of Pre-defined Functions using Spark SQL
    • Validating Functions using Spark SQL
    • String Manipulation Functions using Spark SQL
    • Date Manipulation Functions using Spark SQL
    • Overview of Numeric Functions using Spark SQL
    • Data Type Conversion using Spark SQL
    • Dealing with Nulls using Spark SQL
    • Using CASE and WHEN using Spark SQL
    • Query Example - Word Count using Spark SQL

    Apache Spark 2 using SQL - Pre-defined Functions - Exercises

    • Prepare Users Table using Spark SQL
    • Exercise 1 - Get number of users created per year
    • Exercise 2 - Get the day name of the birth days of users
    • Exercise 3 - Get the names and email ids of users added in the year 2019
    • Exercise 4 - Get the number of users by gender
    • Exercise 5 - Get last 4 digits of unique ids
    • Exercise 6 - Get the count of users based up on country code

    Apache Spark 2 using SQL - Windowing Functions

    • Introduction to Windowing Functions using Spark SQL
    • Prepare HR Database in Spark Metastore using Spark SQL
    • Overview of Windowing Functions using Spark SQL
    • Aggregations using Windowing Functions using Spark SQL
    • LEAD or LAG Functions using Spark SQL
    • Getting first and last values using Spark SQL
    • Ranking using Windowing Functions in Spark SQL
    • Order of execution of Spark SQL Queries
    • Overview of Subqueries using Spark SQL
    • Filtering Window Function Results using Spark SQL

    Sample scenarios with solutions

    • Introduction to Sample Scenarios and Solutions
    • Problem Statements - General Guidelines
    • Initializing the job - General Guidelines
    • Getting crime count per type per month - Understanding Data
    • Getting crime count per type per month - Implementing the logic - Core API
    • Getting crime count per type per month - Implementing the logic - Data Frames
    • Getting crime count per type per month - Validating Output
    • Get inactive customers - using Core Spark API (leftOuterJoin)
    • Get inactive customers - using Data Frames and SQL
    • Get top 3 crimes in RESIDENCE - using Core Spark API
    • Get top 3 crimes in RESIDENCE - using Data Frame and SQL
    • Convert NYSE data from text file format to parquet file format
    • Get word count - with custom control arguments, num keys and file format

    Instructors

    Mr Durga Viswanatha Raju Gadiraju

    Mr Durga Viswanatha Raju Gadiraju
    Technology Adviser
    Freelancer

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