PySpark is a Python-based API for the Apache Spark data processing engine. Find out why you should learn to use this tool, and how to take a PySpark Course.
Data science and Machine Learning open up new possibilities. However, these disciplines require tools capable of processing massive sets of Big Data. This is why solutions are emerging, such as the Spark processing engine and the PySpark API in Python.
What is Apache Spark?
Before discussing PySpark, it’s important to understand what Apache Spark is. It’s an open source framework written in Scala and designed to process large datasets in a distributed cluster.
Thanks to its in-memory processing system, Spark is a hundred times faster. This tool has rapidly established itself as a must-have for Big Data.
What is PySpark?
PySpark is a Python API for Apache Spark. It enables large datasets to be processed in a distributed cluster.
With this tool, it becomes possible to run a Python application using Apache Spark features. This API was developed in response to the massive adoption of Python by the industry, since Spark was originally written in Scala. Thus, PySpark was launched with Python PY4J.
This is a Java library integrated within PySpark, enabling a dynamic interface with JVM objects. It is therefore essential to install Java, Python and Apache Spark to run PySpark.
It is also possible to use the Anaconda distribution for development. Widely used for Machine Learning, it provides a number of very useful tools, such as the Jupyter Spyder IDE notebooks.
Who uses PySpark?
PySpark is widely used in the fields of Data Science and Machine Learning. There are many Data Science libraries written in Python, such as NumPy and TensorFlow.
Several PySpark modules are specially dedicated to Data Science and Machine Learning, including RDD, DataFrame and MLib. It’s an ideal solution for large-scale data analysis and the development of Machine Learning pipelines.
Compared with traditional Python applications, PySpak makes it possible to run Machine Learning applications on billions of data sets on distributed clusters a hundred times faster.
The advantages of PySpark are the simplicity of the Python language, and the various data visualization features. These are just some of the reasons for its success.
Many well-known companies use PySpark, including Amazon, Walmart, Trivago, Sanofi and Runtastic. The tool is used in a wide variety of sectors, including healthcare, finance, education, entertainment and e-commerce.
Why learn to use PySpark / take a PySpark Course?
For Data Science and Machine Learning, PySpark is now considered a must-have tool. Since 2016, the number of job offers requiring mastery of this tool has doubled.
If you want to work in these fields, it’s imperative that you learn how to use PySpark. What’s more, if you’ve already mastered the Python language, learning PySpark won’t be too difficult and will open many doors for you.
Learning to use PySpark will enable you to acquire a highly sought-after, well-paid skill in the corporate world. If you’re thinking of becoming a Data Scientist, this is one of the tools you need to master.
How can I take a PySpark course?
For a PySpark training, you can choose DataScientest training courses. With our Data Scientist training, you’ll learn Python programming.
Machine Learning with PySpark is at the heart of the Big Data module, alongside SQL. The course also covers DataViz, Machine Learning, Deep Learning and AI.
You can complete this training with an intensive BootCamp or Continuing Education if you already have a business. Our remote Blended Learning approach combines 85% individual coaching on a SaaS platform and 15% Masterclass.
At the end of the course, you will receive a certificate issued by MINES ParisTech / PSL Executive Education as part of a partnership. As far as financing is concerned, our programs are eligible for the Compte Personnel de Formation. Don’t wait any longer and discover Data Scientist training!
Now you know everything about PySpark training. Discover our complete dossier on Spark, and our introduction to Machine Learning.
