What you'll learn:
- Setup the Single Node Hadoop and Spark using Docker locally or on AWS Cloud9
- Review ITVersity Labs (exclusively for ITVersity Lab Customers)
- All the HDFS Commands that are relevant to validate files and folders in HDFS.
- Quick recap of Python which is relevant to learn Spark
- Ability to use Spark SQL to solve the problems using SQL style syntax.
- Pyspark Dataframe APIs to solve the problems using Dataframe style APIs.
- Relevance of Spark Metastore to convert Dataframs into Temporary Views so that one can process data in Dataframes using Spark SQL.
- Apache Spark Application Development Life Cycle
- Apache Spark Application Execution Life Cycle and Spark UI
- Setup SSH Proxy to access Spark Application logs
- Deployment Modes of Spark Applications (Cluster and Client)
- Passing Application Properties Files and External Dependencies while running Spark Applications
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 Python as a Programming language. This course used to be a CCA175 Spark and Hadoop Developer course for the preparation for the Certification Exam. As of 10/31/2021, the exam is sunset and we have renamed it to Apache Spark 2 and Apache Spark 3 using Python 3 as it covers industry-relevant topics beyond the scope of certification.
About Data Engineering
Data Engineering is nothing but processing the data depending upon 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 ETLDevelopment, Data Warehouse Development, etc. Apache Spark is evolved as a leading technology to take care of Data Engineering at scale.
Ihave prepared this course for anyone who would like to transition into a Data Engineer role using Pyspark (Python +Spark). Imyself 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. We will provide details about Resources or Environments to learn Spark SQL and PySpark 3 using Python 3 as well as Reference Material on GitHub to practice Spark SQL and PySpark 3 using Python 3. Keep in mind that you can either use the cluster at your workplace or set up the environment using provided instructions or use ITVersity Lab to take this course.
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 Python
This course requires a decent knowledge of Python. To make sure you understand Spark from a Data Engineering perspective, we added a module to quickly warm up with Python. If you are not familiar with Python, then we suggest you go through our other course Data Engineering Essentials - Python, SQL, and Spark.
Master required Hadoop Skills to build Data Engineering Applications
As part of this section, you will primarily focus on HDFScommands so that we can copy files into HDFS. The data copied into HDFS will be used as part of building data engineering pipelines using Spark and Hadoop with Python as the Programming Language.
Overview of HDFSCommands
Copy Files into HDFSusing the put or copyFromLocal command using appropriate HDFSCommands
Review whether the files are copied properly or not to HDFSusing HDFSCommands.
Get the size of the files using HDFS commands such as du, df, etc.
Some fundamental concepts related to HDFS such as block size, replication factor, etc.
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 SQLwill 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 Tables - Basic DDL and DML in Spark SQL
Managing Tables - DML and Create Partitioned Tables using Spark SQL
Overview of Spark SQL Functions to manipulate strings, dates, null values, etc
Windowing Functions using Spark SQL for ranking, advanced aggregations, etc.
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 Apache 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 or Pyspark Data Frame APIs.
Projecting or Selecting data from Spark Data Frames, renaming columns, providing aliases, dropping columns from Data Frames, etc using Pyspark Data Frame APIs.
Processing Column Data using Spark or Pyspark Data Frame APIs - You will be learning functions to manipulate strings, dates, null values, etc.
Basic Transformations on Spark Data Frames using Pyspark Data Frame APIs such as Filtering, Aggregations, and Sorting using functions such as filter/where, groupBy with agg, sort or orderBy, etc.
Joining Data Sets on Spark Data Frames using Pyspark Data Frame APIs such as join. You will learn inner joins, outer joins, etc using the right examples.
Windowing Functions on Spark Data Frames using Pyspark Data Frame APIs to perform advanced Aggregations, Ranking, and Analytic Functions
Spark Metastore Databases and Tables and integration between Spark SQLand Data Frame APIs
Apache Spark Application Development and Deployment Life Cycle
Once you go through the content related to Spark using a Jupyter-based environment, we will also walk you through the details about how the Spark applications are typically developed using Python, deployed as well as reviewed.
Setup Python Virtual Environment and Project for Spark Application Development using Pycharm
Understand complete Spark Application Development Lifecycle using Pycharm and Python
Build zip file for the Spark Application, copy to the environment where it is supposed to run and run.
Understand how to review the Spark Application Execution Life Cycle.
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.
