Apache Spark with Scala for Certified Databricks Professional Training

person icon Bigdata Engineer

Apache Spark with Scala for Certified Databricks Professional Training

Apache Spark with Scala Crash Course useful for Databricks Certification Unofficial for beginners

updated on icon Updated on Sep, 2023

language icon Language - English

person icon Bigdata Engineer

architecture icon Development,Data Science,Apache Spark


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Course Description

Apache Spark with Scala useful for Databricks Certification(Unofficial)

Apache Spark with Scala its a Crash Course for Databricks Certification Enthusiast (Unofficial) for beginners 

“Big data" analysis is a hot and highly valuable skill – and this course will teach you the hottest technology in big data: Apache Spark. Employers including Amazon, eBay, NASA, Yahoo, and many more. All are using Spark to quickly extract meaning from massive data sets across a fault-tolerant Hadoop cluster. You'll learn those same techniques, using your own Operating system right at home.

So, What are we going to cover in this course then?

Learn and master the art of framing data analysis problems as Spark problems through over 30+ hands-on examples, and then execute them up to run on Databricks cloud computing services (Free Service) in this course. Well, the course is covering topics which are included for certification:  

1) Spark Architecture Components 

  • Driver, 

  • Core/Slots/Threads, 

  • Executor 

  • Partitions

2) Spark Execution 

  • Jobs 

  • Tasks 

  • Stages 

3) Spark Concepts 

  • Caching, 

  • DataFrame Transformations vs. Actions, Shuffling 

  • Partitioning, Wide vs. Narrow Transformations   

4) DataFrames API 

  • DataFrameReader 

  • DataFrameWriter 

  • DataFrame [Dataset]

5) Row & Column (DataFrame)

6) Spark SQL Functions 

In order to get started with the course And to do that you're going to have to set up your environment.

So, the first thing you're going to need is a web browser that can be (Google Chrome or Firefox, or Safari, or Microsoft Edge (Latest version)) on Windows, Linux, and macOS desktop 

This is completely Hands-on Learning with the Databricks environment.


What will you learn in this course:

  • Apache Spark ( Spark Core, Spark SQL, Spark RDD and Spark DataFrame)
  • Databricks Certification syllabus included in the Course
  • An overview of the architecture of Apache Spark.
  • Work with Apache Spark's primary abstraction, resilient distributed datasets(RDDs) to process and analyze large data sets.
  • Develop Apache Spark 3.0 applications using RDD transformations and actions and Spark SQL.
  • Analyze structured and semi-structured data using Datasets and DataFrames, and develop a thorough understanding about Spark SQL.


What are the prerequisites for this course?

  • Some programming experience is required and Scala fundamental knowledge is also required , but you need to know the fundamentals of programming in order to pick it up.
  • You will need a desktop PC and an Internet connection.
  • Any flavor of Operating System is fine.
Apache Spark with Scala for Certified Databricks Professional Training


Check out the detailed breakdown of what’s inside the course

Apache Spark with Scala useful for Databricks Certification
38 Lectures
  • play icon Introduction 02:39 02:39
  • play icon Download Resources
  • play icon Introduction to Spark 07:21 07:21
  • play icon (Old) Free Account creation in Databricks 01:51 01:51
  • play icon (New) Free Account Creation in Databricks 01:50 01:50
  • play icon Provisioning a Spark Cluster 02:14 02:14
  • play icon Basics about notebooks 07:29 07:29
  • play icon Why we should learn Apache Spark? 03:08 03:08
  • play icon Spark Architecture Components 07:10 07:10
  • play icon Driver 02:38 02:38
  • play icon Partitions 01:26 01:26
  • play icon Executors 02:48 02:48
  • play icon Spark Jobs 02:10 02:10
  • play icon Spark Stages 00:57 00:57
  • play icon Spark Tasks 00:48 00:48
  • play icon Practical Demonstration of Jobs, Tasks and Stages 03:14 03:14
  • play icon Spark RDD (Create and Display Practical) 18:21 18:21
  • play icon Spark Dataframe (Create and Display Practical) 12:06 12:06
  • play icon Anonymus Functions in Scala 04:38 04:38
  • play icon Extra (Optional on Spark DataFrame) 04:47 04:47
  • play icon Extra (Optional on Spark DataFrame) in Details 12:46 12:46
  • play icon Spark Datasets (Create and Display Practical) 17:01 17:01
  • play icon Caching 02:27 02:27
  • play icon Notes on reading files with Spark 04:16 04:16
  • play icon Data Source CSV File 08:53 08:53
  • play icon Data Source JSON File 06:21 06:21
  • play icon Data Source LIBSVM File 03:52 03:52
  • play icon Data Source Image File 04:44 04:44
  • play icon Data Source Arvo File 02:22 02:22
  • play icon Data Source Parquet File 02:50 02:50
  • play icon Untyped Dataset Operations (aka DataFrame Operations) 03:52 03:52
  • play icon Running SQL Queries Programmatically 02:55 02:55
  • play icon Global Temporary View 03:04 03:04
  • play icon Creating Datasets 03:42 03:42
  • play icon Scalar Functions (Built-in Scalar Functions) Part 1 08:34 08:34
  • play icon Scalar Functions (Built-in Scalar Functions) Part 2 14:12 14:12
  • play icon Scalar Functions (Built-in Scalar Functions) Part 3 14:32 14:32
  • play icon User Defined Scalar Functions 07:15 07:15
Spark RDD
39 Lectures

Instructor Details

Bigdata Engineer

Bigdata Engineer

I am Solution Architect with 12+ year’s of experience in Banking, Telecommunication and Financial Services industry across a diverse range of roles in Credit Card, Payments, Data Warehouse and Data Center programmes

My role as Bigdata and Cloud Architect to work as part of Bigdata team to provide Software Solution.

Responsibilities includes,

- Support all Hadoop related issues
- Benchmark existing systems, Analyse existing system challenges/bottlenecks and Propose right solutions to eliminate them based on various Big Data technologies
- Analyse and Define pros and cons of various technologies and platforms
- Define use cases, solutions and recommendations
- Define Big Data strategy
- Perform detailed analysis of business problems and technical environments
- Define pragmatic Big Data solution based on customer requirements analysis
- Define pragmatic Big Data Cluster recommendations
- Educate customers on various Big Data technologies to help them understand pros and cons of Big Data
- Data Governance
- Build Tools to improve developer productivity and implement standard practices

I am sure the knowledge in these courses can give you extra power to win in life.

All the best!!

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