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Introduction to Apache Spark APIs for Data Processing

Welcome to the website of the course on Apache Spark by CERN IT. The course is self-paced and open, it is a short introduction to the architecture and key abstractions used by Spark. Theory and demos cover the main Spark APIs: DataFrame API, Spark SQL, Streaming, Machine Learning. You will also learn how to deploy Spark on CERN computing resources, notably using the CERN SWAN service. Most tutorials and exercises are in Python and run on Jupyter notebooks.

Apache Spark is a popular engine for data processing at scale. Spark provides an expressive API and a scalable engine that integrates very well with the Hadoop ecosystem as well as with Cloud resources. Spark is currently used by several projects at CERN, notably by IT monitoring, by the security team, by the BE NXCALS project, by teams in ATLAS and CMS. Moreover, Spark is integrated with the CERN Hadoop service, the CERN Cloud service, and the CERN SWAN web notebooks service.

Accompanying notebooks

       Get the notebooks from:

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

o   https://gitlab.cern.ch/db/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 DataFrames with exercises video Icon

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o   Solutions to the exercises

       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:

o   How to monitor Spark execution: slides and video Icon

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

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o   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 in November 2022