Imagine a game of Tetris. Different-shaped blocks flow down. And if you fail to rotate them in time ― you lose. However, if you fill the empty spaces without a single gap, the row is deleted, and you get to live. So your success depends entirely on how you structure random pieces in the game.
The same principle applies to data processing: data from thousands of stores, located in several different countries, flow to data scientists like Tetris blocks. That data is neither combed nor immediately understandable. All the bits are different from one another. There are also empty spaces, repetitions, and errors ― just like in the game. And the amount of data increases every minute ― the flow is endless. To make these datasets useful and profitable for the business, they need to be structured. A powerful tool like PySpark can do that!
What is PySpark?
PySpark consists of two parts: Py — Python and Spark — Apache Spark.
Python is one of the simplest programming languages due to its clear syntax. People use it in apps, software and game development, data science, and machine learning (ML). (You can learn more about Python here.)
And Apache Spark is a framework that can handle large amounts of unstructured data.
A framework is a software package for solving various types of issues. In order to work with it, you will structure your code in a certain way.
Using a framework in software development is like building a house. Sure, you can create a plan for a future house from scratch and without any design experience. However, you’ll probably make a lot of mistakes, and your house will likely not be comfortable to live in. Failure to use any framework in coding will achieve a similar result.
A safer option for an inexperienced designer building a house would be to use a ready-made standard project. In this scenario, the foundation, utility location, walls insulation, and everything else have already been thought out. There’s only one exception: you cannot change the layout.
The same goes for frameworks. The developer uses a ready-made template and just fills it with code. No serious mistakes, no tears, no regrets.
Apache Spark is a powerful framework that can split data processing tasks among several computers, in case there is too much data. It was created using Scala — a special language that gives you even more direct control over Spark.
The functionality of Apache Spark was impressive, so a lot of developers wanted to work with it. But there was a problem: Scala was an unpopular programming language. And no one would learn it, even if it meant the ability to use Spark.
The solution was to make an API that would allow interaction with Apache Spark using other programming languages — such as Java, R, and Python.
API or Application Programming Interfaces are a mechanism that helps two different pieces of software to communicate with each other — or a software and programming language, as in our case.
PySpark, then, is an API that links Python and Apache Spark. And for most situations, it’s enough, and even much easier to work with big data.
Python is one of the most widely used and in-demand programming languages. That is why a program like PySpark is used everywhere: for example, Amazon, Walmart, Trivago, Sanofi, Runtastic, and many others use it to process a lot of heavy data.
Not only did data processing become powerful, but also available to a wide range of IT specialists. Now data scientists who write code in Python can process data in PySpark faster than without it. It saves a lot of time!
What essential functions can PySpark perform
PySpark was created for Big Data and machine learning technology. The latter is a branch of artificial intelligence: it focuses on using data to teach software to become more accurate at predicting outcomes.
Below are some features of PySpark that help it handle billions of data rows.
Real-time data processing. Like the blocks in Tetris, the data stream has no end. There can be terabytes of constantly updated data lines. PySpark can process this data in real time. Other data processing tools — like Pandas — cannot do this.
For instance, if you want to process data in Pandas, you first have to collect the data and load it into the program. PySpark works in a different way. It is an open-source framework that connects to the sources you want to take data from. PySpark will collect the data from these sources and process it in real time. This helps businesses improve their product.
Imagine a company. Let’s call it Pear. A data scientists team constantly collects data about user interactions with the Emma smart speaker. They process it using PySpark.
This tool helped them to reveal an interesting trend. Most users ask for sports scores.
The company developed a special mode for Emma to notify users about major sporting events. As a result, this has led to increased user satisfaction. Consequently, Emma’s rating on Amazon is up 0.64, and quarterly sales are up 43%.
PySpark's ability to process real-time data helped determine what most customers were actually using the speaker for. This resulted in Pear improving its product and growing its revenue.
The multifunctional system. In Tetris, the player does not need to possess special skills. The objective is, simply, to rotate and stack the blocks correctly to fill empty spaces.
The same goes for data scientists working in PySpark. The tool already includes a number of built-in functions and libraries for machine learning, data visualization, and data science.
If IT specialists didn’t use such software, they would spend a lot of time setting up the necessary frameworks and libraries from scratch and end up making a lot of mistakes that cost the company money. So PySpark saves time and the business money at the same time.
To level with you, many data processing tools have multifunctional systems. Without this, modern software cannot stay competitive.
Fast recovery after fault. If you have no moves left in Tetris, the game is over. It doesn’t matter how successfully you played. Fail.
PySpark, luckily, works the other way around. It quickly recovers data that was lost during the crash. In IT terms, it is called fault tolerance.
Imagine there was a crash, and the program shut down.
Unlike Tetris, PySpark caches data records in memory in the proper order. Therefore, an unexpected disconnection will not change the order and logic of the data.
How to master
Before you get started with PySpark, you need to master Python. Learning a new language is a long road. You need to read dozens of documents about Python, its libraries, and other tools connected with it.
You then need to know how to process and analyze the data. With that out of the way, you can install PySpark on your computer and practice with it.
The tutorial by DataBricks also gives a detailed step-by-step explanation of the tool's operations.
However, it's going to be difficult to master these things without a tutor to guide your learning.
To avoid struggling, it’s good to learn PySpark along with other useful data science tools. Our Data Science Bootcamp gives you exactly that. It is a nine-month online beginner-friendly program that teaches essential IT skills, necessary for becoming a highly paid data scientist.
A team consisting of an experienced tutor, code reviewers, and tech support will help level up your game. With our bootcamp, PySpark and other data science tools that used to be beyond understanding no longer seem all that difficult.
Why PySpark is worth your time
PySpark is an effective place to start when it comes to Big Data Processing.
And it’s an open-source tool, so it runs operations on billions and trillions of data ― even faster than similar software. Such things are impossible to do on a local device, as it lacks the necessary processing power.
We hope we’ve demonstrated that data analysis and machine learning on large-scale datasets is a valuable tool to have if you wish to become a data scientist!
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