Description

With the rapid growth in data volume that is being used in data analysis tasks, it gets more and more challenging for the user to process it using standard methods. One typically runs into into several problems - low memory/cpu, waiting forever for a job to complete or starting all over again if a job fails. Enter Spark, a high-performance distributed computing framework, which allows us to tackle big-data problems by distributing the workload across a cluster of machines. The two day course addresses the technical architecture and use cases of Spark, writing Spark code using Python, using Spark's machine learning library to perform ML based tasks. Then, we would be looking at the methods for running a spark cluster on a cloud based infrastructure, along with ways to manage and fine tune your cluster. The course will also demonstrate how to work with real-time data streams.

The first day includes the overview, architectural concepts, programming with Spark's fundamental data structure (RDD) and Spark's Machine Learning library. The second day focuses on the analysis of data by running SQL queries in Spark, working with real-time data streams and how to setup and manage a spark cluster.

Learning outcome

After the course the participants should be able to write simple to intermediate programmes in Spark using RDD and dataframes.

Intended Audience and Prerequisites

The course is intended for researchers, students, and professionals with programming skills, preferably in Python, as the exercises are in Python. Some knowledge of SQL is also recommended.
 

Please NOTE: This is not a regular programming course, participants would be expected to learn emerging concepts in the field of big data / distributed processing, which might be completely different from the concepts of a general programming language.

Agenda

Day 1, Monday 19.11

   09.00 – 09.45    Overview and architechture of Spark
   09:45 – 10.30    Basics of RDDs and Demo
   10.30 – 10.45    Coffee break
   10.45 – 11.30    RDD: Transformations and Actions
   11.30 – 12.00    Exercises
   12.00 – 13.00    Lunch
   13.00 – 13.30    Word Count Example
   13.30 – 14.00    Exercises
   14.00 – 14.30    Short overviewof Machine learning library of Spark
   14.30 – 14.45    Coffee break
   14.45 – 15.30    Exercises
   15.30 – 15.45    Wrap-up and further topics
   15.45 – 16.00    Summary of the first day & exercises walk-through
Day 2, Tuesday, 20.11

   09.00 – 09.30    Spark Dataframes and SQL Overview
   09:30 – 10.15    Exercises
   10.15 – 10.30    Coffee break
   10.30 – 10.45    Dataframes and SQL (contd.)
   10.45 – 12.00    Exercises
   12.00 – 13.00    Lunch
   13.00 – 14.00    Setting up a Spark cluster
   14.00 – 14.30    Exercises
   14.00 – 14.30    Best practices and other useful stuff
   14.30 – 14.45    Coffee break
   14.45 – 15.00    Brief overview of Spark Streaming
   15.00 – 15.15    Demo: Processing live twitter stream data
   15.15 – 16.00    Summary of the course & exercises walk-through
Lecturers: 

Apurva Nandan (CSC, lecturer), Juha Hulkkonen (CSC, teaching assistant)

Language:   EnglishPrice:          Free of charge

https://events.prace-ri.eu/event/785/