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Spark SQL & Hadoop (For Data Science)

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Spark SQL & Hadoop (For Data Science), available at $69.99, has an average rating of 4.15, with 85 lectures, based on 70 reviews, and has 5567 subscribers.

You will learn about Students will get hands-on experience working in a Spark Hadoop environment that’s free and downloadable as part of this course. Students will have opportunities solve Data Engineering and Data Analysis Problems using Spark on a Hadoop cluster in the sandbox environment that comes as part Issuing HDFS commands. Converting a set of data values in a given format stored in HDFS into new data values or a new data format and writing them into HDFS. Loading data from HDFS for use in Spark applications & writing the results back into HDFS using Spark. Reading and writing files in a variety of file formats. Performing standard extract, transform, load (ETL) processes on data using the Spark API. Using metastore tables as an input source or an output sink for Spark applications. Applying the understanding of the fundamentals of querying datasets in Spark. Filtering data using Spark. Writing queries that calculate aggregate statistics. Joining disparate datasets using Spark. Producing ranked or sorted data. This course is ideal for individuals who are This course has been designed specifically for data scientists, big data analysts and data engineers looking to leverage the power of Hadoop and Apache Spark to make sense of big data. or This course is also well suited for university students and recent graduates that are keen to land a job with a company that’s looking to fill a big data-related positions or anyone who simply wants to apply their SQL skills in a big data environment using Spark-SQL. or Software engineers & developers who are looking to break into the Data Engineering field will also find this course helpful. It is particularly useful for This course has been designed specifically for data scientists, big data analysts and data engineers looking to leverage the power of Hadoop and Apache Spark to make sense of big data. or This course is also well suited for university students and recent graduates that are keen to land a job with a company that’s looking to fill a big data-related positions or anyone who simply wants to apply their SQL skills in a big data environment using Spark-SQL. or Software engineers & developers who are looking to break into the Data Engineering field will also find this course helpful.

Enroll now: Spark SQL & Hadoop (For Data Science)

Summary

Title: Spark SQL & Hadoop (For Data Science)

Price: $69.99

Average Rating: 4.15

Number of Lectures: 85

Number of Published Lectures: 85

Number of Curriculum Items: 85

Number of Published Curriculum Objects: 85

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

  • Students will get hands-on experience working in a Spark Hadoop environment that’s free and downloadable as part of this course.
  • Students will have opportunities solve Data Engineering and Data Analysis Problems using Spark on a Hadoop cluster in the sandbox environment that comes as part
  • Issuing HDFS commands.
  • Converting a set of data values in a given format stored in HDFS into new data values or a new data format and writing them into HDFS.
  • Loading data from HDFS for use in Spark applications & writing the results back into HDFS using Spark.
  • Reading and writing files in a variety of file formats.
  • Performing standard extract, transform, load (ETL) processes on data using the Spark API.
  • Using metastore tables as an input source or an output sink for Spark applications.
  • Applying the understanding of the fundamentals of querying datasets in Spark.
  • Filtering data using Spark.
  • Writing queries that calculate aggregate statistics.
  • Joining disparate datasets using Spark.
  • Producing ranked or sorted data.

Who Should Attend

  • This course has been designed specifically for data scientists, big data analysts and data engineers looking to leverage the power of Hadoop and Apache Spark to make sense of big data.
  • This course is also well suited for university students and recent graduates that are keen to land a job with a company that’s looking to fill a big data-related positions or anyone who simply wants to apply their SQL skills in a big data environment using Spark-SQL.
  • Software engineers & developers who are looking to break into the Data Engineering field will also find this course helpful.

Target Audiences

  • This course has been designed specifically for data scientists, big data analysts and data engineers looking to leverage the power of Hadoop and Apache Spark to make sense of big data.
  • This course is also well suited for university students and recent graduates that are keen to land a job with a company that’s looking to fill a big data-related positions or anyone who simply wants to apply their SQL skills in a big data environment using Spark-SQL.
  • Software engineers & developers who are looking to break into the Data Engineering field will also find this course helpful.

Apache Spark is currently one of the most popular systems for processing big data.

Apache Hadoop continues to be used by many organizations that look to store data locally on premises. Hadoop allows these organisations to efficiently store big datasets ranging in size from gigabytes to petabytes.

As the number of vacancies for data science, big data analysis and data engineering roles continue to grow, so too will the demand for individuals that possess knowledge of Spark and Hadoop technologies to fill these vacancies.

This course has been designed specifically for data scientists, big data analysts and data engineers looking to leverage the power of Hadoop and Apache Spark to make sense of big data.

This course will help those individuals that are looking to interactively analyse big data or to begin writing production applications to prepare data for further analysis using Spark SQL in a Hadoop environment.

The course is also well suited for university students and recent graduates that are keen to gain exposure to Spark & Hadoop or anyone who simply wants to apply their SQL skills in a big data environment using Spark-SQL.

This course has been designed to be concise and to provide students with a necessary and sufficient amount of theory, enough for them to be able to use Hadoop & Spark without getting bogged down in too much theory about older low-level APIs such as RDDs.

On solving the questions contained in this course students will begin to develop those skills & the confidence needed to handle real world scenarios that come their way in a production environment.

(a) There are just under 30 problems in this course. These cover hdfs commands, basic data engineering tasks and data analysis.

(b) Fully worked out solutions to all the problems.

(c) Also included is the Verulam Blue virtual machine which is an environment that has a spark Hadoop cluster already installed so that you can practice working on the problems.

  • The VM contains a Spark Hadoop environment which allows students to read and write data to & from the Hadoop file system as well as to store metastore tables on the Hive metastore.

  • All the datasets students will need for the problems are already loaded onto HDFS, so there is no need for students to do any extra work.

  • The VM also has Apache Zeppelin installed. This is a notebook specific to Spark and is similar to Python’s Jupyter notebook.

This course will allow students to get hands-on experience working in a Spark Hadoop environment as they practice:

  • Converting a set of data values in a given format stored in HDFS into new data values or a new data format and writing them into HDFS.

  • Loading data from HDFS for use in Spark applications & writing the results back into HDFS using Spark.

  • Reading and writing files in a variety of file formats.

  • Performing standard extract, transform, load (ETL) processes on data using the Spark API.

  • Using metastore tables as an input source or an output sink for Spark applications.

  • Applying the understanding of the fundamentals of querying datasets in Spark.

  • Filtering data using Spark.

  • Writing queries that calculate aggregate statistics.

  • Joining disparate datasets using Spark.

  • Producing ranked or sorted data.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: The Udemy Environment

Chapter 2: Introduction to Hadoop & Spark

Lecture 1: Section Introduction

Lecture 2: Big Data

Lecture 3: Distributed Storage & Processing

Lecture 4: Introduction to Hadoop

Lecture 5: Introduction to Spark

Lecture 6: Spark Applications

Lecture 7: Spark's Interactive Shell

Lecture 8: Distributed Processing on a Hadoop Cluster using Spark

Chapter 3: Our Working Environment

Lecture 1: Section Introduction

Lecture 2: Install Oracle VM VirtualBox

Lecture 3: The Verulam Blue VM – Zipped Files for Downloading

Lecture 4: Loading the Verulam Blue VM

Lecture 5: Booting up the VM

Lecture 6: Spin Up Cluster

Lecture 7: spark-shell

Lecture 8: Run Zeppelin Notebook

Lecture 9: Problems & practice test questions

Chapter 4: HDFS Basic File Management

Lecture 1: Interacting with HDFS

Lecture 2: The File System Shell (FS Shell)

Lecture 3: Commands and operations -help

Lecture 4: Commands and operations -ls

Lecture 5: Commands and operations -find

Lecture 6: Commands and operations -mkdir

Lecture 7: Commands and operations -put

Lecture 8: Commands and operations -cp -mv

Lecture 9: Commands and operations -cat -tail -text

Lecture 10: Commands and operations -rmdir -rm

Lecture 11: Commands and operations -get

Lecture 12: Health warning

Lecture 13: HDFS Basic File Management – Problems & Solutions

Chapter 5: Data Structures

Lecture 1: Section Introduction

Lecture 2: DataFrames

Lecture 3: Tables

Lecture 4: Temp Views

Chapter 6: Spark SQL & Creating Data Structures

Lecture 1: Section Introduction

Lecture 2: Querying Data Structures using SQL via Spark SQL

Lecture 3: Creating DataFrames with Spark SQL

Lecture 4: Creating Databases & Tables with Spark SQL

Lecture 5: Creating Temporary Views with Spark SQL

Chapter 7: Basic Operations on Data Structures

Lecture 1: Section Introduction

Lecture 2: Operations on DataFrame columns

Lecture 3: Operations on DataFrame rows

Lecture 4: Basic SQL queries for Tables

Chapter 8: Data Engineering

Lecture 1: Section Introduction

Lecture 2: The ETL Process

Lecture 3: The Extract Phase of an ETL process

Lecture 4: The Extract Phase – Loading CSV and Text files

Lecture 5: The Extract Phase – Loading JSON and Parquet files

Lecture 6: The Extract Phase – Loading Avro and ORC files

Lecture 7: The Transform Phase of an ETL process

Lecture 8: The Transform Phase – String Transformations

Lecture 9: The Transform Phase – Numerical Transformations

Lecture 10: The Transform Phase – Date & Time Transformations

Lecture 11: The Transform Phase – Data Type Transformations

Lecture 12: The Transform Phase – Transformations of Nulls

Lecture 13: The Load Phase of an ETL process

Lecture 14: The Load Phase – Saving DataFrame data to Files I

Lecture 15: The Load Phase – Saving DataFrame data to Files II

Lecture 16: The Load Phase – Saving DataFrame data to Tables

Lecture 17: Data Engineering – Solutions to Problems

Chapter 9: Data Analysis

Lecture 1: Section Introduction

Lecture 2: Metastore Tables as Input Sources or Output Sinks

Lecture 3: Querying datasets in Spark

Lecture 4: Math Functions in SQL

Lecture 5: Filtering

Lecture 6: Sorting & Ranking

Lecture 7: Aggregation

Lecture 8: Grouping

Lecture 9: Multi Table Queries

Lecture 10: Multi Table Queries – Joins

Lecture 11: Multi Table Queries – Types of Joins

Lecture 12: Multi Table Queries – Unions

Lecture 13: Data Analysis – Solutions to Problems

Chapter 10: End of Course Test Solutions

Lecture 1: End of Course Test Solutions

Chapter 11: Appendix – Hadoop Theory

Lecture 1: HDFS Architecture

Lecture 2: YARN Architecture

Chapter 12: Appendix – Spark Theory

Lecture 1: Components of a Spark application

Lecture 2: The Driver Process

Lecture 3: The Executor Process

Lecture 4: The Master Process

Lecture 5: The Spark Application Execution Model

Lecture 6: Deploying Spark Applications on Hadoop clusters

Chapter 13: ** BONUS SECTION **

Lecture 1: ** BONUS **

Instructors

Rating Distribution

  • 1 stars: 1 votes
  • 2 stars: 1 votes
  • 3 stars: 12 votes
  • 4 stars: 12 votes
  • 5 stars: 44 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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