Spark SQL and Spark 3 using Scala Hands-On with Labs
A comprehensive course on Spark SQL as well as Data Frame APIs using Scala with complementary lab access
What you’ll learn
All the HDFS Commands that are relevant to validate files and folders in HDFS.
Enough Scala to work Data Engineering Projects using Scala as Programming Language
Spark Dataframe APIs to solve the problems using Dataframe style APIs.
Basic Transformations such as Projection, Filtering, Total as well as Aggregations by Keys using Spark Dataframe APIs
Inner as well as outer joins using Spark Data Frame APIs
Ability to use Spark SQL to solve the problems using SQL style syntax.
Basic Transformations such as Projection, Filtering, Total as well as Aggregations by Keys using Spark SQL
Inner as well as outer joins using Spark SQL
Basic DDL to create and manage tables using Spark SQL
Basic DML or CRUD Operations using Spark SQL
Create and Manage Partitioned Tables using Spark SQL
Manipulating Data using Spark SQL Functions
Advanced Analytical or Windowing Functions to perform aggregations and ranking using Spark SQL
Basic programming skills
Self support lab (Instructions provided) or ITVersity lab at additional cost for appropriate environment.
Minimum memory required based on the environment you are using with 64 bit operating system
4 GB RAM with access to proper clusters or 16 GB RAM with virtual machines such as Cloudera QuickStart VM
As part of this course, you will learn all the key skills to build Data Engineering Pipelines using Spark SQL and Spark Data Frame APIs using Scala as a Programming language. This course used to be a CCA 175 Spark and Hadoop Developer course for the preparation of the Certification Exam. As of 10/31/2021, the exam is sunset and we have renamed it to Spark SQL and Spark 3 using Scala as it covers industry-relevant topics beyond the scope of certification.
About Data Engineering
Data Engineering is nothing but processing the data depending on our downstream needs. We need to build different pipelines such as Batch Pipelines, Streaming Pipelines, etc as part of Data Engineering. All roles related to Data Processing are consolidated under Data Engineering. Conventionally, they are known as ETL Development, Data Warehouse Development, etc. Apache Spark is evolved as a leading technology to take care of Data Engineering at scale.
I have prepared this course for anyone who would like to transition into a Data Engineer role using Spark (Scala). I myself am a proven Data Engineering Solution Architect with proven experience in designing solutions using Apache Spark.
Let us go through the details about what you will be learning in this course. Keep in mind that the course is created with a lot of hands-on tasks which will give you enough practice using the right tools. Also, there are tons of tasks and exercises to evaluate yourself.
Setup of Single Node Big Data Cluster
Many of you would like to transition to Big Data from Conventional Technologies such as Mainframes, Oracle PL/SQL, etc and you might not have access to Big Data Clusters. It is very important for you set up the environment in the right manner. Don’t worry if you do not have the cluster handy, we will guide you through support via Udemy Q&A.
- Setup Ubuntu-based AWS Cloud9 Instance with the right configuration
- Ensure Docker is setup
- Setup Jupyter Lab and other key components
- Setup and Validate Hadoop, Hive, YARN, and Spark
Are you feeling a bit overwhelmed about setting up the environment? Don’t worry!!! We will provide complementary lab access for up to 2 months. Here are the details.
- Training using an interactive environment. You will get 2 weeks of lab access, to begin with. If you like the environment, and acknowledge it by providing a 5* rating and feedback, the lab access will be extended to additional 6 weeks (2 months). Feel free to send an email to [email protected] to get complementary lab access. Also, if your employer provides a multi-node environment, we will help you set up the material for the practice as part of the live session. On top of Q&A Support, we also provide required support via live sessions.
A quick recap of Scala
This course requires a decent knowledge of Scala. To make sure you understand Spark from a Data Engineering perspective, we added a module to quickly warm up with Scala. If you are not familiar with Scala, then we suggest you go through relevant courses on Scala as Programming Language.
Data Engineering using Spark SQL
Let us, deep-dive into Spark SQL to understand how it can be used to build Data Engineering Pipelines. Spark with SQL will provide us the ability to leverage distributed computing capabilities of Spark coupled with easy-to-use developer-friendly SQL-style syntax.
- Getting Started with Spark SQL
- Basic Transformations using Spark SQL
- Managing Spark Metastore Tables – Basic DDL and DML
- Managing Spark Metastore Tables Tables – DML and Partitioning
- Overview of Spark SQL Functions
- Windowing Functions using Spark SQL
Data Engineering using Spark Data Frame APIs
Spark Data Frame APIs are an alternative way of building Data Engineering applications at scale leveraging distributed computing capabilities of Spark. Data Engineers from application development backgrounds might prefer Data Frame APIs over Spark SQL to build Data Engineering applications.
- Data Processing Overview using Spark Data Frame APIs leveraging Scala as Programming Language
- Processing Column Data using Spark Data Frame APIs leveraging Scala as Programming Language
- Basic Transformations using Spark Data Frame APIs leveraging Scala as Programming Language – Filtering, Aggregations, and Sorting
- Joining Data Sets using Spark Data Frame APIs leveraging Scala as Programming Language
All the demos are given on our state-of-the-art Big Data cluster. You can avail of one-month complimentary lab access by reaching out to [email protected] with a Udemy receipt.
Who this course is for:
- Any IT aspirant/professional willing to learn Data Engineering using Apache Spark
- Python Developers who want to learn Spark using Scala to add additional skill to be a Data Engineer
- Java or Scala Developers to learn Spark using Scala to add Data Engineering Skills to their profile
Created by Durga Viswanatha Raju Gadiraju, Asasri Manthena
Last updated 2/2022
Size: 8.74 GB