Getting Started with Spark 2 from Pluralsight

The 2.x releases of Spark represent significantly different and upgraded features. This course will focus on all of these changes, in both theory and practice.

Spark is possibly the most popular engine for big data processing these days and the 2.x release has several new features which make Spark more powerful and easy to work with. In this course, Getting Started with Spark 2, you will get up and running with Spark 2 and understand the similarities and differences between version 2.x and older versions. First, you will get to see the basic Spark architecture and the details of Project Tungsten which brought great performance improvements to Spark 2. You will go over the new developer APIs using DataFrames and see how they inter-operate with RDDs from Spark 1.x. Next, you will move on to big data processing where you will load and clean datasets, remove invalid rows, execute transformations to extract insights and perform grouping, sorting, and aggregations using the new DataFrame APIs. You will also study how and where to use broadcast variables and accummulators. Finally, you will work with Spark SQL which allows you to use SQL commands for big data processing. The course also covers advanced SQL support in the form of windowing operations. At the end of this course, you should be very comfortable working with Spark DataFrames and Spark SQL. You will be better equipped to make technical choices based on the performance trade-offs of older versions of Spark vs. Spark 2. Software required: Apache Spark 2.2, Python 2.7.

What's inside

Syllabus

Course Overview

Understanding Differences Between Spark 2.x and Spark 1.x

Exploring and Analyzing Data with DataFrames

Querying Data Using Spark SQL

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops a solid foundation for beginners with data processing using Spark 2.x

Teaches Spark 2.x, which is highly relevant to industry

Emphasizes both theoretical and practical aspects of Spark 2.x

Uses Spark DataFrames and Spark SQL, which are essential for data processing

Incorporates interactive materials and hands-on labs for practical skills development

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Getting Started with Spark 2 with these activities:

Review 'Learning Spark: Lightning-Fast Data Analytics'

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Reinforce your understanding of Spark concepts and best practices by reviewing this comprehensive guide.

View Learning Spark: Lightning-Fast Big Data Analysis on Amazon

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Read Chapter 2: Spark Architecture and Internals
Review Chapter 4: DataFrames and Datasets
Skim Chapter 6: SQL and DataFrames

Connect with Spark professionals at industry meetups

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Expand your professional network and gain insights from experienced Spark practitioners.

Browse courses on Networking

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Identify and attend relevant meetups or conferences
Introduce yourself and engage in conversations
Share your knowledge and ask questions
Follow up with connections and explore potential collaborations

Attend a workshop on Spark 2 optimization techniques

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Gain insights and hands-on experience in optimizing Spark applications from industry experts.

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Research and identify a relevant workshop
Register and attend the workshop
Actively participate and ask questions
Apply the learned techniques to your own Spark projects

Two other activities

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Follow tutorials on advanced Spark SQL features

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Explore advanced SQL features such as windowing operations to enhance data analysis capabilities.

Browse courses on Spark SQL

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Identify scenarios where windowing functions can enhance data analysis
Learn different windowing functions and their syntax
Practice using windowing functions in Spark SQL queries

Optimize Spark applications

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Gain practical experience in optimizing Spark applications for better performance.

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Identify performance bottlenecks in Spark applications
Apply techniques to improve performance such as caching, partitioning, and using optimized data structures
Measure and track performance improvements

Career center

Learners who complete Getting Started with Spark 2 will develop knowledge and skills that may be useful to these careers:

Big Data Architect

Big Data Architects design and build data architectures for businesses. As a Big Data Architect, you will use your knowledge of Spark to design and implement big data solutions for businesses. This course can help you become a Big Data Architect because it provides hands-on experience with the latest version of Spark, a popular big data processing engine.

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Software Engineer - Big Data

Software Engineers specializing in big data are responsible for designing, implementing, and maintaining software that processes large datasets. As a Software Engineer in Big Data, you will use your knowledge of Apache Spark to handle the technical aspects of big data management, including performance optimization and data analytics. This course will help you become effective in writing code for Spark, as it provides hands-on practice in how to use the latest version of Spark.

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Data Engineer

Data Engineers design and build data pipelines that move data from source systems to data warehouses and other destinations. As a Data Engineer, you will use your knowledge of Spark to develop and manage data pipelines for businesses. This course can help you become a Data Engineer because it provides hands-on experience with the latest version of Spark, which is a leading big data processing engine.

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Data Analyst

Data Analysts manipulate data to discover trends and patterns and help businesses make informed decisions. As a Data Analyst, you can use Spark to perform complex data analysis on large datasets, and you will be able to stay on top of the industry by being familiar with the latest Spark developments, which this course will help you do.

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Database Administrator

Database Administrators manage and maintain databases, including the hardware and software used to store and process data. As a Database Administrator, you will use your knowledge of Spark to manage and maintain big data databases. This course can help you become a Database Administrator because it provides hands-on experience with the latest version of Spark, a popular big data processing engine.

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Data Scientist

Data Scientists analyze data to help companies make better decisions and are essential for large corporations seeking insights into marketing campaigns, sales targets, and financial planning. Gaining experience with Apache Spark, one of the industry standard data processing engines for big data, will give you a huge advantage in your job search. This course can help you become a Data Scientist because it focuses on the latest version of Spark, which represents significant upgrades to Spark's features and functions.

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Machine Learning Engineer

Machine Learning Engineers design and build machine learning models that can learn from data and make predictions. As a Machine Learning Engineer, you will use Spark to develop and train machine learning models on large datasets. This course can help you become a Machine Learning Engineer because it provides hands-on experience with the latest version of Spark, which is a popular big data processing engine used in machine learning.

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Data Architect

Data Architects design and manage data systems, including the hardware and software used to store and process data. As a Data Architect, you will use your knowledge of Spark to design and implement big data solutions for businesses. This course can help you become a Data Architect because it provides hands-on experience with the latest version of Spark, a big data processing engine that is used in many industries.

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Software Developer

Software Developers design, build, and maintain software applications. As a Software Developer, you will use your knowledge of Spark to develop and maintain big data applications. This course can help you become a Software Developer because it provides hands-on experience with the latest version of Spark, a popular big data processing engine.

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Data Science Manager

Data Science Managers lead and manage teams of data scientists and other professionals who work with big data. As a Data Science Manager, you will use your knowledge of Spark to manage and coordinate big data projects. This course can help you become a Data Science Manager because it provides hands-on experience with the latest version of Spark, a popular big data processing engine.

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Software Engineer (Data Science)

Software Engineers specializing in Data Science develop and maintain software applications that use data science techniques. As a Software Engineer in Data Science, you will use your knowledge of Spark to develop and maintain big data applications. This course can help you become a Software Engineer in Data Science because it provides hands-on experience with the latest version of Spark, a popular big data processing engine.

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Statistician

Statisticians collect, analyze, and interpret data to provide insights into a variety of topics. As a Statistician, you will use Spark to analyze large datasets and identify trends and patterns. This course can help you become a Statistician because it provides hands-on experience with the latest version of Spark, a popular big data processing engine.

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Business Analyst

Business Analysts analyze business processes and data to improve efficiency and effectiveness. As a Business Analyst, you will use your knowledge of Spark to analyze large datasets and identify trends and patterns. This course can help you become a Business Analyst because it provides hands-on experience with the latest version of Spark, a popular big data processing engine.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical methods to analyze data, and are often employed in finance to help make investment decisions. As a Quantitative Analyst, you will use Spark to perform data analysis on large datasets, such as financial time series and market data. The course will provide you with a solid foundation in the latest version of Spark, which will make you a more competitive candidate for Quantitative Analyst roles.

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Research Analyst

Research Analysts conduct research and provide insights on a variety of topics, including market trends, economic conditions, and industry analysis. As a Research Analyst, you will use Spark to analyze large datasets, such as consumer surveys and market research data. By taking this course, you will become familiar with the latest version of Spark, which will make you a more competitive candidate for Research Analyst roles.

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