PySpark Vs. Python: What's The Difference?

Both PySpark vs. Python have an expansive range of data analysis tools available to them. While each language has its own set of strengths and weaknesses, it can be difficult to figure out which tool will work best in any given situation. If you're trying to decide between the two, this guide provides an easy-to-understand breakdown of how the two languages compare side-by-side. Plus, it details strategies on when you should use each one, as well as lists resources that you can use to start learning both PySpark and Python today!

Spark Overview

Spark is a cluster computing framework with two core components—an engine for cluster-based parallel data processing, and a programming library that allows developers to write applications in Java, Scala, or Python. In order to work with Spark, you have to write code in one of these three languages (there are also community contributions available in R and other languages). Each language has its own pros and cons; here’s an overview of how they stack up against each other on key elements like memory usage, reliability, versatility, etc.

Spark Installation

Spark runs in a cluster, not on individual machines. You will need to install and set up Spark in a Hadoop environment, but once you do, it can run programs like any other Java or Scala program. (If you are working on Windows, Spark comes pre-packaged with Mesos/Marathon.) If you've ever done any sort of machine learning before, getting started with PySpark is as simple as installing Python and Spark and adding a single line to your existing code. Just make sure that wherever you download Spark that it matches your version of python (ex: 2.7). Don't get it mixed up!

Creating Your First Spark Project

Before you dig into any code, you’ll need to set up Spark locally on your machine and create a Spark project that can be used to run an application. Thankfully, creating a new Spark project is relatively simple and easy to do in a few steps. Creating Your First Application: Once you have your Spark environment set up, it’s time to start writing some code! The first step in building a Spark application is defining your input sources and output sinks. This step is generally more involved than subsequent ones because there are many configurations you must take into account when designing your dataflow. Loading Data: Next, once you've specified how to get data into Spark (e.g., via HDFS), it's time to load that data so you can process it with Spark. Since most of Spark’s functionality revolves around processing collections of objects rather than individual objects, loading data typically involves breaking down larger sets of files or rows into smaller chunks - such as dividing large files into individual lines or rows of values. Processing Data: After loading your data, the real fun begins!

Understanding the Spark Shell

Spark is a high-level abstraction used for doing parallel computing in a cluster environment. Spark’s primary compute engine is known as MapReduce, which was first introduced by Google and has since become an industry standard for processing large amounts of data in parallel. These days there are many tools that use map reduce like Apache Hive or Pig (which are also both built on top of Hadoop). A few years ago, though, there were fewer options available and it wasn’t uncommon to see new technologies be built directly atop MapReduce to take advantage of its power. One such technology is called PySpark (sometimes referred to as just pyspark). Like its predecessor Pig, PySpark makes it easy to write code for big data platforms like Hadoop—with far less overhead than pure Java code would provide. It comes with interactive shells (analogous to iPython) that allow you access to all facets of Hadoop without having to be proficient at Java or Scala.

Comparing RDDs to Pandas DataFrames

To Spark, or not to Spark; that is definitely a question you should be asking yourself when considering whether to use RDDs or Pandas DataFrames in your own data science projects. As far as tools go, PySpark and Pandas are about as different as night and day (or panda fur and bonfire sparks). RDDs—most commonly referred to simply as Spark—were created by using Scala libraries before being incorporated into Jupyter notebooks; on the other hand, DataFrames were created by using NumPy arrays before becoming part of Pandas library for quick integration with Jupyter notebooks. Both Spark and DataFrames have their advantages for performing data-related analysis tasks, but which one is better?

Conclusion

Spark is an incredible tool for data scientists and programmers, but that doesn’t mean it’s right for every type of data processing. Python, on other hand, is versatile enough to be used across industries, and has a number of tools available to help process different types of data sets. Understanding both of these technologies can help you determine which is best suited for your unique problem-solving needs. No one solution will work in all cases, so it helps to know what options are out there. Here is the leading platform where you can avail the best python development company in India at a reliable cost.