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How to Learn Spark

Have you ever come across a data-processing task? Or, do you ever wonder how terabytes of data are processed and stored in distributed storage systems? Such tasks require a powerful, intelligent management solution to help reduce costs and increase efficiency. Spark is one such open-source distributed computing solution — it helps quickly store and process Big Data.

In this guide, we’re going to talk about how to learn Spark and what resources you can use to master it.

What You Need to Know About Spark

Spark is a leading, open-source cluster computing software framework. It is extremely popular with programmers and data scientists working with Big Data. It is known to be so efficient that programs written using Spark can run up to 100 times faster than Hadoop’s MapReduce jobs. Spark offers easy-to-use APIs that abstract away much of the tedious tasks of distributed computing and big data handling. Some of the key concepts that you will need to keep an eye out for on your journey to learning Spark are:

  • RDD (Resilient Distributed Dataset). It is the way in which data abstraction is implemented in Spark. RDDs are immutable, distributed collections of data points that can be stored in memory or storage. They are partitioned across multiple devices to facilitate parallel operation using low-level APIs.
  • Dataframe. Similar to RDDs, dataframes are immutable, distributed collections of data. However, dataframes resemble relation tables in their structure. The data stored in a data frame is organized into named columns.
  • Dataset. These were introduced recently to help in simplifying transformations carried out on the stored objects, preserving the performance and benefits of the Spark SQL execution engine at the same time.
  • MLlib. Spark also houses a general machine learning library that is designed to be simple, scalable, and offer seamless integration with other tools. Given the performance and capabilities of Spark, data problems can be solved faster by running data science tasks on this technology.
  • ML Pipelines. Usually, machine learning tasks involve running a sequence of subtasks, which includes pre-processing, feature extraction, and model fitting. Even though there are multiple libraries that can be used at various sub-stages, connecting all of this is not as easy as it looks. The ML Pipelines is a high-level API that helps do this job.
  • GraphX. When it comes to handling graphs and graph-parallel computation, GraphX is the Apache component you would need. At a high-level, GraphX extends the RDDs and exposes a set of fundamental graph operators that easily helps carry out these operations.
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These are only a few of the many things the technology offers. As you learn more about Spark, you’ll become aware of more things you can use to help speed up your system’s development.

Skills Needed to Learn Spark

To learn Spark, you should have a basic understanding of distributed computing. 

You do not need to be an expert in distributed computing and Big Data processing to understand Spark, but having a preliminary understanding of the concepts will help you easily get started. Having prior experience with any other distributed computing technology like Hadoop is a bonus.

Why You Should Learn Spark

If you work in the domain of distributed computing or Big Data processing, there is a long list of reasons why you should consider learning Spark. Spark is known for its simplicity and accessibility for working with data at scale. Spark is made to be fast in accessing data from both memory and storage. This gives it an immense performance boost when compared to other distributed computing technologies.

If you are looking to process and analyze data, Spark is one of the best available alternatives. Due to its high speed and reliability, it is gradually replacing other distributed processing technologies. If you are looking to start your career in the world of data processing and analysis, Spark is one of the best skills to pick up early.

How Long Does It Take to Learn Spark?

Spark is a relatively tougher skill to pick when compared with other technologies. If you are looking to get started with the core Spark APIs and are a quick learner, one week of training is sufficient to get you going. If you are looking to learn Spark well enough to become confident for interviews, you can expect at least two months of regular training and self-practice.

All in all, you can expect to devote time and resources to learning for a period of four to six weeks to get a good grip on the language. Mastering any distributed computing technology is no easy task, and the same goes for Spark. To start building real-life systems with Spark, you can expect to spend about two to three months working on the details of the technology.

Learning Spark: A Study Guide

You will find plenty of Spark learning resources online. With so much information available, you may be wondering where exactly you should start. We have compiled a list of five learning resources to help you learn what you need to know about the Spark platform.

Spark Starter Kit

  • Resource Type: Video Course
  • Platform: Udemy
  • Price: Free (Online)
  • Prerequisites: None

Spark Starter Kit is a free video course available on Udemy as a three-hour long tutorial. It is a good place for newcomers to begin at, as it opens with a comparative analysis between Hadoop and Spark — two distributed computing systems. The course then proceeds to explain other concepts of Spark, like memory management, fault tolerance, etc. This helps in building a solid foundation of distributed systems before getting hands dirty with code.

Scala and Spark 2 – Getting Started

  • Resource Type: Video Course
  • Platform: Udemy
  • Price: Free (Online)
  • Prerequisites: None

This course is a good sequel to the previous one on the list, as it aims to explain how to set up your local machine to create Spark applications. It uses the Scala programming language to install and set up distributed applications. If you are looking to convert your theoretical understanding of Spark into an actual project, this course is the way to go.

Apache Spark Fundamentals

  • Resource Type: Video Course
  • Platform: PluralSight
  • Price: Requires PluralSight Subscription
  • Prerequisites: Some experience with Spark or other distributed systems

This course teaches Spark from the ground up, starting with its history before creating a Wikipedia analysis application as one of the means for learning a wide scope of its core API. That core knowledge makes it easier to look into Spark’s other libraries, such as the streaming and SQL APIs. Towards the end, the course explains how to avoid a few commonly encountered rough edges of Spark.

Learning Spark: Lightning-Fast Big Data Analysis

  • Resource Type: Book
  • Price: $35.89
  • Prerequisites: None

Learning Spark has been written by the developers of Spark. It helps get data scientists and engineers up and running in no time. The book teaches how to express parallel jobs with just a few lines of code, and covers applications from simple batch jobs to stream processing and machine learning.

It helps readers in diving quickly into Spark’s primary capabilities, which include distributed datasets and in-memory caching. Towards the end, the book ensures to help readers master advanced concepts like data partitioning and shared variables as well.

Spark GraphX in Action

  • Resource Type: Book
  • Price: $23.99
  • Prerequisites: Some prior experience with Spark or any other distributed computing system

This book is meant for those having an intermediate level of experience in Spark. Without wasting any time, it dives right into the GraphX graph processing API. It leverages examples to teach how to configure and use GraphX interactively. Along the way, you are sure to collect valuable experience on how to approach and solve machine learning problems using Apache Spark.

Communities for People Studying Spark

Communities are an excellent resource for anyone who wants to learn Spark. By joining a community, you can quickly find help. You can also learn more about how other people use Spark, which may inform how you use the tool.

Below we have created a list of some top communities for people studying Spark that you may want to look at in more detail.

Spark Community

Run by the Apache organization, the Spark community is a hub of knowledge. You will find a list of resources to connect with the folks working with Spark and a collection of sites where you can post your questions and get answers from experienced users. There is also a chatroom directory. You can use this directory to find rooms where content related to Spark is discussed at great lengths.

Databricks Spark Forum

Databricks has a discussion forum on Spark for users to ask and answer questions. The community is currently small but growing. It is a great place to post doubts and questions that you feel are common for other users as well.

How Hard is It to Learn Spark?

Spark is a distributed computing system, which brings with itself a lot of complex theoretical concepts to understand first. Spark is on the advanced end of the list of available distributed computing solutions, with features that beat most of the modern distributed technologies. It is evident that learning Spark is sort of an uphill task. But the rewards are generous, as Spark rates among the top distributed technologies in demand today.

To simplify the learning process, you can try covering the core concepts of Spark like dataframes and datasets first. Once you have a solid grip on these basics, adding to this knowledge will be comparatively easier. Alternatively, you can take a look at some of the resources listed above for beginner-friendly courses and tutorials to take your first steps in the world of Spark.

Will Learning Spark Help Me Find a Job?

Spark is a highly sought-after database skill in the technology industry. Employers hiring for software development and database administration positions often list Spark as an essential skill or an important qualification. To help you understand the value of learning Spark for your career, we have compiled a few job and salary statistics.

  • Salaries. PayScale reports that jobs that involve Spark pay, on average, $111,053 per year. Positions that use this skill include data engineer, data scientist, and software engineer.
  • Industry Growth. It has been observed that job postings for data engineers have gone up by 400% since 2012, and they have been almost doubled in the last year itself. This shows that data engineering and analysis are here to stay for long. While not all of these positions use Spark, a considerable number of these professionals are likely to use distributed computing systems similar to Spark.

Conclusion: Should You Learn Spark?

Spark is a distributed computing model that makes handling big data super fast and easy. Using Spark, the biggest shortcomings of Hadoop 1 — implementing SQL queries and processing streaming operations — can be overcome with ease. This makes it a great upgrade over Hadoop.

Spark is going to be very useful for you if you wish to pursue a career in data science. It helps you to build robust fast systems easily, and its powerful querying capabilities ensure that you are not missing out on high performance in any aspect.

With ever-growing salaries and strong career growth projections, Spark holds the potential to add a lot of value to your data-focused career.

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