Apache Spark 2 and 3 using Python 3 (Formerly CCA 175)

During this course, you will learn how to use Spark SQL and Spark Data Frame APIs to build data pipelines. You will also learn how to use Python to write code. A CCA 175 Spark and Hadoop Developer course used to be called this one, but now it’s called CCA 175 Spark and Hadoop Developer. As of October 31, 2021, the exam will no longer be available. We have changed the name of the exam to Apache Spark 2 and 3 using Python 3 because it covers important topics that aren’t covered in the certification.

About Data Engineering

Data engineering is just making the data work for us in the future. Part of data engineering is to build different pipelines, like Batch Pipelines and Streaming pipes. We need to do this to make sure that our data is clean. All jobs that deal with data processing are combined into one job called Data Engineering. They are called ETL Development, Data Warehouse Development, and so on in the past. Apache Spark has become the best way to do Data Engineering at a large scale with a lot of data.

I have made this course for anyone who wants to become a Data Engineer with Pyspark (Python + Spark). I myself am a proven Data Engineering Solution Architect who has worked with Apache Spark before.

In this class, we’ll go over what you’ll learn and why. Keep in mind that the course has a lot of hands-on tasks that will help you learn how to use the right tools. This isn’t the only way you can check your own progress. There are a lot of tasks and exercises for that, too.

Setting up a single-node Big Data Cluster

A lot of you would rather move from traditional technologies like Mainframes and Oracle PL/SQL to Big Data. You might not be able to use Big Data Clusters because you don’t have the money for them. I think it is very important for you to set things up in the right way. Do not worry if you don’t have the cluster with you. We will help you through Udemy Q&A to show you how to do it.

• Set up an Ubuntu-based AWS Cloud9 Instance with the right settings, then start it.
• If you want to use Docker, make sure that it is set up first.
• Set up Jupyter Lab and other important parts.
• Set up and make sure it works. Hadoop, Hive, YARN, and Spark are some of the things.

A quick review of Python.

This class is for people who know a lot about Python. In order to make sure you understand Spark from a Data Engineering point of view, we added a module that helps you get used to Python quickly. You might want to check out our Data Engineering Essentials – Python, SQL, and Spark course if you don’t already know how to work with Python.

People who do data engineering with the help of Spark SQL

Spark SQL is a great tool for building Data Engineering Pipelines. Let’s take a look at how it can be used. Spark with SQL will let us use the distributed computing power of Spark with easy-to-use developer-friendly SQL-style syntax.

• Making Your First Spark SQL Queries
• Using Spark SQL, you can make simple changes.
• In this lesson, we’ll learn how to manage Spark Metastore Tables.
• People who work with Spark Metastore Tables: DML and Partitioning.
• In this video, we show you how to use the Spark SQL functions.
• Use Spark SQL to do windowing functions.

Engineers can do data work with APIs from Spark called Data Frames.

Spark Data Frame APIs are another way to build Data Engineering applications at a large scale with the help of Spark’s distributed computing. Data Engineers who have backgrounds in application development might choose Data Frame APIs over Spark SQL to build Data Engineering apps.

• Data Processing with Spark Data Frame APIs.
• Using Spark Data Frame APIs, we can process column data.
• Filtering, Aggregation, and Sorting are some of the basic transformations you can do with Spark Data Frame APIs.
• Using Spark Data Frame APIs, you can join Data Sets.
• Aggregations, Ranking, and Analytical Functions that use Spark Data Frame APIs.
• Databases and tables in the Spark Metastore.

The development and deployment of Apache Spark apps People go through different stages in their lives.

As of Apache Spark-based Data Engineers, we should know about the Application Development and Deployment Lifecycle, which is what we do. As part of this section, you’ll learn about the whole development and deployment life cycle, from start to finish. It includes, but isn’t limited to, making the code work in the real world, putting properties outside of the code, and more.

• The development lifecycle of an Apache Spark application
• This is what happens when you run an application and use Spark’s UI.
• Set up an SSH Proxy to get to Spark Application logs.
• Deployment Modes for Spark Apps
• Application Properties Files and External Dependencies are read.