Getting Started with Spark 2

The 2.x releases of Spark represent significantly different and upgraded features. This course will focus on all of these changes, in both theory and practice.
Course info
Level
Beginner
Updated
May 16, 2018
Duration
2h 16m
Table of contents
Understanding Differences Between Spark 2.x and Spark 1.x
Exploring and Analyzing Data with DataFrames
Querying Data Using Spark SQL
Course Overview
Description
Course info
Level
Beginner
Updated
May 16, 2018
Duration
2h 16m
Description

Spark is possibly the most popular engine for big data processing these days and the 2.x release has several new features which make Spark more powerful and easy to work with. In this course, Getting Started with Spark 2, you will get up and running with Spark 2 and understand the similarities and differences between version 2.x and older versions. First, you will get to see the basic Spark architecture and the details of Project Tungsten which brought great performance improvements to Spark 2. You will go over the new developer APIs using DataFrames and see how they inter-operate with RDDs from Spark 1.x. Next, you will move on to big data processing where you will load and clean datasets, remove invalid rows, execute transformations to extract insights and perform grouping, sorting, and aggregations using the new DataFrame APIs. You will also study how and where to use broadcast variables and accummulators. Finally, you will work with Spark SQL which allows you to use SQL commands for big data processing. The course also covers advanced SQL support in the form of windowing operations. At the end of this course, you should be very comfortable working with Spark DataFrames and Spark SQL. You will be better equipped to make technical choices based on the performance trade-offs of older versions of Spark vs. Spark 2. Software required: Apache Spark 2.2, Python 2.7.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi, my name is Janani Ravi, and welcome to this course on Getting Started with Spark 2. A little about myself, I have a master's degree in electrical engineering from Stanford, and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. In this course, you'll get up and running with Spark 2, and understand the similarities and differences between version 2. X and older versions. You'll understand the basic Spark architecture and the details of Project Tungsten, which brought great performance improvements to Spark 2. The course will cover the new developer APIs using DataFrames, and you'll see how they interoperate with RDDs from Spark 1. We'll then move on to big data processing, where we'll load and clean Datasets, remove invalid rows, execute transformations to extract insights, and perform grouping, sorting, and aggregations using the new DataFrame APIs. We'll also study how and where to use broadcast variables and accumulators. We'll then work with Spark SQL, which allows us to use SQL commands for big data processing. Datasets loaded into Spark can be used to retrieve information using the familiar SQL constructs. The course also covers advanced SQL support in the form of windowing operations. At the end of this course, you should be very comfortable working with Spark DataFrames and Spark SQL. You should be able to make technical choices based on performance tradeoffs of older versions of Spark versus Spark 2.