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Introduction to Apache Spark APIs for Data Processing

Welcome to our Apache Spark course, presented by CERN IT! This course offers a comprehensive yet concise exploration of Apache Spark's architecture and its fundamental components. Designed to be self-paced and accessible to all, it delves into the core Spark APIs including DataFrame API, Spark SQL, Streaming, and Machine Learning, blending theoretical insights with practical demonstrations. Key highlights of the course:

Hands-On Learning: Engage in interactive tutorials and exercises predominantly in Python, utilizing Jupyter notebooks for a seamless learning experience.

Real-World Application: Discover how to efficiently deploy Spark on CERN’s computing infrastructure, especially leveraging the CERN SWAN service.

Industry Relevance: Understand how Spark, a leading engine for large-scale data processing, is effectively integrated within various projects at CERN, including IT monitoring, security, the BE NXCALS project, and by teams in ATLAS and CMS, as well as its synergy with the Hadoop service and Cloud resources at CERN.

By the end of this course, you’ll have a solid understanding of how Spark operates within complex systems like those at CERN, and how it can be applied to a range of data processing challenges. Whether you’re a beginner or looking to enhance your existing knowledge, this course is tailored to provide a foundational understanding of Spark's capabilities and real-world applications.

Accompanying notebooks

·       Get the notebooks from:

o   https://github.com/cerndb/SparkTraining

o   https://gitlab.cern.ch/hadoop/training/SparkTraining

·       How to run the notebooks:

o   CERN SWAN (recommended option):

o   Colab , Binder

o   Local/private Jupyter notebook

o   See also the SWAN gallery and the video:

 

Course lectures and tutorials

·       Introduction and objectives: slides and video

         


·       Session 1: Apache Spark fundamentals

o   Lecture “Spark architecture and intro to DataFrames”: slides and video

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o   Notebooks:

o   Tutorial on Spark DataFrames with exercises – video Icon

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o   Solutions to the exercises

o   Examples of Pandas on Spark

·       Session 2: Working with Spark DataFrames and SQL

o   Lecture “Introduction to Spark SQL”: slides and video



o   Notebooks:

o   Tutorial on Spark SQL – video Icon

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o   Exercises on Spark SQL

o   Solutions to the exercises

·       Session 3: Building on top of the DataFrame API

o   Lecture “Spark as a Data Platform”: slides and video



o   Lecture “Spark Streaming”: slides and video



o   Lecture “Spark and Machine Learning”: slides and video



o   Notebooks:

o   Tutorial on Spark Streaming – video Icon

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o   Tutorial on Spark Machine Learning – regression task – video Icon

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o   Tutorial on Spark Machine Learning – classification task with the Higgs dataset

o   Demo of the Spark JDBC data source how to read Oracle tables from Spark

o   Note on Spark and Parquet format

·       Session 4: How to scale out Spark jobs

o   Lecture “Running Spark on CERN resources”: slides and video



o   Notebooks:

o   Demo on using SWAN with Spark on Hadoop – video Icon

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o   Demo of Spark processing Physics data using CERN private Cloud resources – video Icon

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o   Example notebook for the NXCALS project

 

Bonus material

·       How to monitor Spark execution: slides and video Icon

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·       Spark as a library, examples of how to use Spark in Scala and Python programs: code and video Icon

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·       Next steps: reading material and links, miscellaneous Spark notes

 

·       Read and watch at your pace:

o   Download the course material for offline use:
 slides.zip, github_repo.zip, videos.zip

o   Watch the videos on YouTube Logo, icon

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Acknowledgements and feedback

Author and contact for feedback and questions: Luca Canali - Luca.Canali@cern.ch

CERN-IT Spark and data analytics services

Former contributors: Riccardo Castellotti, Prasanth Kothuri

Many thanks to CERN Technical Training for their collaboration and support

 

License: CC BY-SA 4.0

Published: November 2022

Last modified: December 2023