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 CERNs computing infrastructure, especially leveraging the CERN SWAN service.
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, youll 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 are 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
o
Notebooks:
o
Tutorial
on Spark DataFrames with exercises video 
· 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
· 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 
o
Tutorial on Spark
Machine Learning regression task video 
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
o
Demo of Spark
processing Physics data using CERN private Cloud resources video 
o
Notebook example
on how to run the TPC-DS
benchmark at scale with Spark and Hadoop
o
Notebook
for the NXCALS project (data extraction)
o
Example for
NXCALS project (vector data and timestamps)
Bonus material
· How to monitor Spark execution: slides and video 
· Spark as a library, examples of how to use Spark in Scala and Python
programs: code
and video 
· 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 
Acknowledgements and feedback
Author and contact: Luca
Canali - Luca.Canali@cern.ch
CERN-IT Spark and Data Analytics
Services
Former contributors: R.
Castellotti, P. Kothuri
Many thanks to CERN Technical
Training for their collaboration and support
License: CC BY-SA 4.0
Published: November 2022
Last modified: March 2024