Description
If you need a fast engine for large data processing, Apache Spark should be your first option on the list. It has multiple advantages over other frameworks for processing Big data. This Apache Spark tutorial is here for you to learn to use it practically. You’ll build a real employee attrition prediction model to find out the top reasons why employees leave an organization. Join in to learn a useful tool ready to use for HR analytics straight away.
What are the main benefits of taking an Apache Spark tutorial?
- Apache Spark tutorial is necessary if you want to learn the most advanced and popular product of the Apache community.
- It’s swift at processing structured and unstructured data, using the in-memory computation.
- It’s dynamic, making it easy to develop parallel applications by providing 80 high-level operators.
- It’s reusable, fault tolerant, and cost efficient.
- And there are many other advantages endorsed by a broad community of developers.
What are you going to learn in this Apache Spark tutorial?
Your end result of learning how to use Spark in this tutorial is going to be implementing the employee attrition prediction machine learning (ML) model. You’re going to use the Databricks platform. To do that, there will be several steps that you’ll need to learn and practice, and I’m here to guide you all the way. You’ll find a collection of downloadable content that will help you progress faster in this Spark tutorial.
- We’ll start by looking at Apache Spark, setting up a Spark cluster, and getting started with that.
- You’ll proceed by learning how to use that Spark cluster to take the data coming into it, how to process that data using a Spark ML model, and generate output in the form of a prediction.
- You’ll master the process of loading, processing, and working with data.
- You’ll discover what makes Apache Spark a perfect tool for machine learning jobs.
- You’ll work on training a machine learning model, testing, and evaluating.
- There will be a lot of input from me, explaining each line of code carefully, and then loads of hands-on practice implementing what you’ll learn.
- You’ll be using the Databricks platform. It will allow us to focus on the data problems by letting us write the Spark ML code instantly.
My goal is to provide you with practical tools that will be beneficial for you in the future. While doing that, you’ll develop a model with a real use opportunity.
Beginners’ course on how to use Spark
To enter this course, you need to have a basic understanding of Spark, SQL, and machine learning. It’s intended for:
- Beginner Apache Spark developers
- Machine learning engineers
- Data scientists
- Big data engineers or developers
- Software developers
Learning how to use Spark will increase your value in the job market since you’ll have a skill suitable for many different qualifications. Enroll in this Apache Spark tutorial today, and let’s start learning!
What Will You Learn?
- You'll implement Spark Machine Learning model using Databricks platform
- You'll learn to launch an Apache Spark cluster
- You'll be able to process big data using a Machine Learning model
- You'll gain a lot of practice
Requirements
- Apache Spark, SQL, and Machine Learning basics
Curriculum
Course consist of total 2h 19min of content, in total.
Section 1: Course
About the Instructor
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 include,
- 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
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