Introduction to Spark 2.0

author: Reynold Xin, Databricks Inc.
author: Michael Armbrust, Databricks Inc.
author: Doug Bateman, Databricks Inc.
author: Matei Zaharia, Computer Science Department, Stanford University
published: Sept. 16, 2016,   recorded: August 2016,   views: 129
Categories

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

 Watch videos:   (click on thumbnail to launch)

Watch Part 1
Part 1 45:47
!NOW PLAYING
Watch Part 2
Part 2 1:08:06
!NOW PLAYING
Watch Part 3
Part 3 1:22:39
!NOW PLAYING

Description

Originally started as an academic research project at UC Berkeley, Apache Spark is one of the most popular open source projects for big data analytics. Over 1000 volunteers have contributed code to the project; it is supported by virtually every commercial vendor; many universities are now offering courses on Spark.

Spark has evolved significantly since the 2010 research paper: its foundational APIs are becoming more relational and structural with the introduction of the Catalyst relational optimizer, and its execution engine is developing quickly to adopt the latest research advances in database systems such as whole-stage code generation.

This tutorial is designed for academic researchers (graduate students, faculty members, and industrial researchers) interested in a brief hands-on overview of Spark. This tutorial covers the core APIs for using Spark 2.0, including DataFrames, Datasets, SQL, streaming and machine learning pipelines. Each topic includes slide and lecture content along with hands-on use of a Spark cluster through a web-based notebook environment. In addition, we will dive into the engine internals to discuss architectural design choices and their implications in practice. We will guide the audience to "hack" Spark by extending its query optimizer to speed up distributed join execution.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: