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With Spark, data is read into memory, operations are performed, and the results are written back, resulting in faster execution. Learn core principles and common packages on DataCamp.
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Recommended for Spark beginners

Build your Spark skills with interactive courses curated by real-world experts

course

Introduction to PySpark

IntermediateSkill Level
4 hours
1.3K
Learn to implement distributed data management and machine learning in Spark using the PySpark package.

track

Big Data with PySpark

25 hours
62
Master how to process big data and leverage it efficiently with Apache Spark using the PySpark API.

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course

Introduction to PySpark

IntermediateSkill Level
4 hours
1.3K
Learn to implement distributed data management and machine learning in Spark using the PySpark package.

course

Machine Learning with PySpark

AdvancedSkill Level
4 hours
365
Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.

course

Feature Engineering with PySpark

AdvancedSkill Level
4 hours
310
Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.

Related resources on Spark

blog

The Top 20 Spark Interview Questions

Essential Spark interview questions with example answers for job-seekers, data professionals, and hiring managers.
Tim Lu's photo

Tim Lu

blog

Flink vs. Spark: A Comprehensive Comparison

Comparing Flink vs. Spark, two open-source frameworks at the forefront of batch and stream processing.
Maria Eugenia Inzaugarat's photo

Maria Eugenia Inzaugarat

8 min

tutorial

Pyspark Tutorial: Getting Started with Pyspark

Discover what Pyspark is and how it can be used while giving examples.
Natassha Selvaraj's photo

Natassha Selvaraj

10 min


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Frequently asked questions

Which Spark course is the best for absolute beginners?

For new learners, DataCamp has three introductory Spark courses across the most popular programming languages:

Introduction to PySpark 

Introduction to Spark with sparklyr in R 

Introduction to Spark SQL in Python Course

Do I need any prior experience to take a Spark course?

You’ll need to have completed an introduction course to the programming language you’re using Spark on. 

All of which you can find here:

Introduction to Python

Introduction to R

Introduction to SQL

Beyond that, anyone can get started with Spark through simple, interactive exercises on DataCamp.

What is PySpark used for?

If you're already familiar with Python and libraries such as Pandas, then PySpark is a good language to learn to create more scalable analyses and pipelines.

Apache Spark is basically a computational engine that works with huge sets of data by processing them in parallel and batch systems. 

Spark is written in Scala, and PySpark was released to support the collaboration of Spark and Python.

How can Spark help my career?

You’ll gain the ability to analyze data and train machine learning models on large-scale datasets—a valuable skill for becoming a data scientist. 

Having the expertise to work with big data frameworks like Apache Spark will set you apart.

What is Apache Spark?

Apache Spark is an open-source, distributed processing system used for big data workloads. 

It utilizes in-memory caching, and optimized query execution for fast analytic queries against data of any size. 

It provides development APIs in Java, Scala, Python, and R, and supports code reuse across multiple workloads—batch processing, interactive queries, real-time analytics, machine learning, and graph processing.

Other technologies and topics

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