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2020-12-06 04:29:42
30-Day Money-Back Guarantee

This course includes:

  • 29 hours on-demand video
  • 7 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
IT & Software IT Certification Hadoop

CCA 175 - Spark and Hadoop Developer Certification - Scala

Cloudera Certified Associate Spark and Hadoop Developer using Scala as Programming Language
Bestseller
Rating: 4.3 out of 54.3 (2,105 ratings)
16,664 students
Created by Durga Viswanatha Raju Gadiraju, Itversity Support, Hindu Varma Datla, Teja Rayala
Last updated 11/2020
English
Italian [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Entire curriculum of CCA Spark and Hadoop Developer
  • HDFS Commands
  • Scala Fundamentals
  • Core Spark - Transformations and Actions
  • Spark SQL and Data Frames
Curated for the Udemy for Business collection

Requirements

  • Basic programming skills
  • Cloudera Quickstart VM or valid account for IT Versity Big Data labs or any Hadoop clusters where Hadoop, Hive and Spark are well integrated.
  • Minimum memory required based on the environment you are using with 64 bit operating system

Description

CCA 175 Spark and Hadoop Developer is one of the well recognized Big Data certification. This scenario based certification exam demands basic programming using Python or Scala along with Spark and other Big Data technologies.

This comprehensive course covers all aspects of the certification using Scala as programming language.

  • Scala Fundamentals

  • Core Spark - Transformations and Actions

  • Spark SQL and Data Frames

  • File formats

  • Flume, Kafka and Spark Streaming

  • Apache Sqoop

Exercises will be provided to prepare before attending the certification. Intention of the course is to boost the confidence to attend the certification.

All the demos are given on our state of the art Big Data cluster. You can avail one week complementary lab access by filling the form which is provided as part of the welcome message.

Who this course is for:

  • Any IT aspirant/professional willing to learn Big Data and give CCA 175 certification

Featured review

Pavol Vadkerti
Pavol Vadkerti
25 courses
12 reviews
Rating: 5.0 out of 5a year ago
Really one of the "from zero to hero" courses. I was typing all the commands like in the video, making notes and had an ITversity lab account and still it took me 4 Months to finish the course (did it in my free time). The instructor was very skilled, easy to understand. I'm looking forward to the next course. Big thumb up!

Course content

15 sections • 196 lectures • 29h 3m total length

  • Preview08:01

  • Preview10:51
  • Setup Scala on Windows
    07:23
  • Basic Programming Constructs
    18:53
  • Functions
    18:35
  • Object Oriented Concepts - Classes
    17:42
  • Object Oriented Concepts - Objects
    13:02
  • Object Oriented Concepts - Case Classes
    11:14
  • Collections - Seq, Set and Map
    08:56
  • Basic Map Reduce Operations
    14:08
  • Setting up Data Sets for Basic I/O Operations
    04:23
  • Basic I/O Operations and using Scala Collections APIs
    16:23
  • Tuples
    04:56
  • Development Cycle - Developing Source code
    07:24
  • Development Cycle - Compile source code to jar using SBT
    09:32
  • Development Cycle - Setup SBT on Windows
    02:48
  • Development Cycle - Compile changes and run jar with arguments
    04:21
  • Development Cycle - Setup IntelliJ with Scala
    12:07
  • Development Cycle - Develop Scala application using SBT in IntelliJ
    10:50

  • Introduction and Curriculum
    05:45
  • Setup Environment - Options
    01:45
  • Setup Environment - Locally
    02:03
  • Setup Environment - using Cloudera Quickstart VM
    07:22
  • Using Windows - Putty and WinSCP
    10:33
  • Using Windows - Cygwin
    14:46
  • HDFS Quick Preview
    20:24
  • YARN Quick Preview
    09:53
  • Setup Data Sets
    08:09

  • Introduction
    05:15
  • Introduction to Spark
    02:22
  • Setup Spark on Windows
    23:15
  • Quick overview about Spark documentation
    04:49
  • Initializing Spark job using spark-shell
    18:39
  • Create Resilient Distributed Data Sets (RDD)
    13:40
  • Previewing data from RDD
    17:57
  • Reading different file formats - Brief overview using JSON
    09:34
  • Transformations Overview
    04:02
  • Manipulating Strings as part of transformations using Scala
    13:44
  • Row level transformations using map
    18:09
  • Row level transformations using flatMap
    09:19
  • Filtering the data
    18:03
  • Joining data sets - inner join
    10:34
  • Joining data sets - outer join
    17:29
  • Aggregations - Getting Started
    04:07
  • Aggregations - using actions (reduce and countByKey)
    15:14
  • Aggregations - understanding combiner
    06:50
  • Aggregations using groupByKey - least preferred API for aggregations
    21:13
  • Aggregations using reduceByKey
    07:36
  • Aggregations using aggregateByKey
    18:21
  • Sorting data using sortByKey
    19:35
  • Global Ranking - using sortByKey with take and takeOrdered
    12:47
  • By Key Ranking - Converting (K, V) pairs into (K, Iterable[V]) using groupByKey
    06:21
  • Get topNPrices using Scala Collections API
    10:49
  • Get topNPricedProducts using Scala Collections API
    11:29
  • Get top n products by category using groupByKey, flatMap and Scala function
    06:02
  • Set Operations - union, intersect, distinct as well as minus
    19:39
  • Save data in Text Input Format
    15:13
  • Save data in Text Input Format using Compression
    11:28
  • Saving data in standard file formats - Overview
    10:23
  • Revision of Problem Statement and Design the solution
    04:12
  • Solution - Get Daily Revenue per Product - Launching Spark Shell
    10:08
  • Solution - Get Daily Revenue per Product - Read and join orders and order_items
    17:46
  • Solution - Get Daily Revenue per Product - Compute daily revenue per product id
    13:41
  • Solution - Get Daily Revenue per Product - Read products data and create RDD
    15:22
  • Solution - Get Daily Revenue per Product - Sort and save to HDFS
    26:17
  • Solution - Add spark dependencies to sbt
    08:01
  • Solution - Develop as Scala based application
    25:34
  • Solution - Run locally using spark-submit
    09:03
  • Solution - Ship and run it on big data cluster
    13:21

  • Introduction to Setting up Enviroment for Practice
    03:09
  • Overview of ITVersity Boxes GitHub Repository
    03:11
  • Creating Virtual Machine
    10:31
  • Starting HDFS and YARN
    04:28
  • Gracefully Stopping Virtual Machine
    05:41
  • Undertanding Datasets provided in Virtual Machine
    05:38
  • Using GitHub Content for the practice
    05:11
  • Using Resources for Practice
    05:10

  • Introduction for the module
    02:18
  • Starting Spark Context
    10:14
  • Overview of Spark read APIs
    18:16
  • Previewing Schema and Data
    04:31
  • Overview of Data Frame APIs
    07:41
  • Overview of Functions
    18:15
  • Overview of Spark Write APIs
    16:43

  • Introduction to Pre-defined Functions
    05:51
  • Creating Spark Session Object in Notebook
    01:55
  • Create Dummy Data Frames for Practice
    08:06
  • Categories of Functions
    02:16
  • Using Special Functions - col
    13:50
  • Using Special Functions - lit
    04:44
  • String Manipulation Functions - Case Conversion and Length
    06:44
  • String Manipulation - Extracting data from fixed lengith fields using substring
    13:16
  • String Manipulation - Extracting data from delimited fields using split
    09:04
  • String Manipulation - Concatenating Strings
    03:37
  • String Manipulation - Padding Strings
    11:10
  • String Manipulation - Trimming unwanted characters
    05:23
  • Date and Time Functions - Overview
    04:14
  • Date and Time Functions - Date Arithmetic
    09:53
  • Date and Time Functions - Using trunc and date_trunc for to date reports
    07:34
  • Date and Time Functions - Using date_format and other functions
    15:33
  • Date and Time Functions - dealing with unix timestamp
    08:13
  • Pre-defined Functions - Conclusion
    04:23

  • Introduction to Basic Transformations using Data Frame APIs
    02:51
  • Starting Spark Context
    03:13
  • Overview of Filtering
    05:24
  • Filtering - Reading Data and Understanding Schema
    02:30
  • Filtering Data - Task 1 - Equal Operator
    08:19
  • Filtering Data - Task 2 - Comparison Operators
    03:41
  • Filtering Data - Task 3 - Boolean AND
    05:22
  • Filtering Data - Task 4 - IN Operator
    05:43
  • Filtering Data - Task 5 - Between and Like
    09:09
  • Filtering Data - Task 6 - Using functions in Filter
    09:48
  • Overview of Aggregations
    08:41
  • Overview of Sorting
    02:52
  • Solution - Get Delayed Counts - Part 1
    06:47
  • Solution - Get Delayed Counts - Part 2
    05:22
  • Solution - Getting Delayed Counts By Date
    16:28

  • Prepare and Validate Data Sets
    04:43
  • Starting Spark Session or Spark Context
    03:28
  • Analyze Data Sets for Joins
    06:11
  • Eliminate Duplicate records from Data Frame
    04:11
  • Recap of Basic Transformations
    04:27
  • Joining Data Sets - Problem Statements
    02:11
  • Overview of Joins
    01:43
  • Inner Join - Get number of flights departed from US airports
    09:17
  • Inner Join - Get number of flights departed from US States
    05:08
  • Outer Join - Get Aiports - Never Used
    07:44

  • Getting Started - Overview
    02:01
  • Overview of Spark Documentation
    02:29
  • Launching and using Spark SQL CLI
    04:08
  • Overview of Spark SQL Properties
    08:51
  • Running OS Commands using Spark SQL
    03:19
  • Understanding Warehouse Directory
    04:12
  • Managing Spark Metastore Databases
    10:01
  • Managing Spark Metastore Tables
    03:21
  • Retrieve Metadata of Tables
    02:19
  • Role of Spark Metastore or Hive Metastore
    05:01
  • Exercise - Getting Started with Spark SQL
    08:57

Instructors

Durga Viswanatha Raju Gadiraju
Technology Adviser and Evangelist
Durga Viswanatha Raju Gadiraju
  • 4.2 Instructor Rating
  • 9,007 Reviews
  • 166,535 Students
  • 18 Courses

13+ years of experience in executing complex projects using vast array of technologies including Big Data and Cloud.

ITVersity, Inc. - a US based organization to provide quality training for IT professionals and we have the track record of training hundreds of thousands of professionals globally.

Building IT career for people with required tools such as high quality material, labs, live support etc to upskill and cross skill is paramount for our organization.

At this time our training offerings are focused on following areas:

* Application Development using Python and SQL

* Big Data and Business Intelligence

* Cloud

* Datawarehousing, Databases

Itversity Support
Support Account for ITVersity Courses.
Itversity Support
  • 4.2 Instructor Rating
  • 8,368 Reviews
  • 150,768 Students
  • 15 Courses

We have built a team to support going forward. If you send messages to this account for our courses, they will be sent to our Helpdesk from where we will be rewriting to our team.

Hindu Varma Datla
Software Engineer at ITVersity
Hindu Varma Datla
  • 4.2 Instructor Rating
  • 3,848 Reviews
  • 45,789 Students
  • 4 Courses

3+ years of IT Experience in the areas of Python using Django as well as Flask, Spark, Linux, SQL using any RDBMS, Java Script, Node JS, Mongo DB etc.

I will be primarily providing support for Python, SQL and other related courses as co-instructor to ITVersity courses.

ITVersity, Inc. - a US based organisation to provide quality training for IT professionals and we have the track record of training hundreds of thousands of professionals globally.

Building IT career for people with required tools such as high quality material, labs, live support etc to up skill and cross skill is paramount for our organisation.

At this time our training offerings are focused on following areas:

* Application Development using Python and SQL

* Big Data and Business Intelligence

* Cloud

* Data Warehousing, Databases

Teja Rayala
Software Engineer at ITVersity Inc.
TR
  • 4.2 Instructor Rating
  • 3,671 Reviews
  • 22,992 Students
  • 3 Courses

Experienced Data Engineer with a demonstrated history of working in the consumer goods industry. Skilled in Apache Airflow, Apache Kafka, Hive, Apache Spark, and Amazon Web Services (AWS). Strong information technology professional with a Master's degree focused in Analytics from University of Cincinnati.

ITVersity, Inc. is a US-based organisation providing quality training for IT professionals and we have a track record of training hundreds of thousands of professionals globally.

Helping build IT careers of people with high-quality content, Labs, live support etc. to upskill and cross-skill is paramount for our organisation.

I will be overseeing the support for ITVersity courses related to Data Engineering and DevOps Engineering

At this time our training offerings are focused on the following areas:

* Application Development using Python and SQL

* Big Data and Business Intelligence

* Cloud

* Data Warehousing, Databases

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