// Databricks notebook source exported at Fri, 17 Jun 2016 03:29:41 UTC

Scalable Data Science

prepared by Raazesh Sainudiin and Sivanand Sivaram

supported by and

The html source url of this databricks notebook and its recorded Uji Image of Uji, Dogen's Time-Being:

sds/uji/week1/01_introduction/001_whySpark

Outline

I. 21 Easy Steps for Sharing your AWS Educate Credits

II. 7 Steps to the Databricks Cloud

III. Essentials of the Databricks Cloud

I. Contributing your AWS credits to the course’s databricks cluster.

Paul, add the steps for AWS credit sharing here.

II. 7 Steps to the Databricks Cloud

Step 1: go to http://www.math.canterbury.ac.nz/databricks and login using your email address and temporary password given to you in person (now).

Step 2: Change your password immediately (now).

Step 3: recognize your Home area in Workspace where you can read and write.

Step 4: cloning the scalable-data-science/week1 folder

Step 5: rename the cloned week1 (*) folder as week1 for simplicity.

Note: From week 2 onwards, you only need to clone the folder for that week (to preserve any changes you made to the notebooks from previous weeks).

Step 6: loading the 003_scalaCrashCourse notebook

Step 7: Attaching 003_scalaCrashCourse notebook to the databricks clusters

UC-enrolled students connect to studentsEnrolled cluster.

others plese connect to studentsObserving1 cluster.

in the example below our mock student has connected to the classCluster cluster.

Finally, you are ready to use the notebook in your own Workspace and follow along the material being covered, execute cells, modify examples and try them out right away, take extra notes in mark-down enhanced via latex, etc.

III. Essentials of Databricks Cloud (DBC)

DBC Essentials: What is Databricks Cloud?

DB workspace, spark, platform

DBC Essentials: Shard, Cluster, Notebook and Dashboard

DB workspace, spark, platform

DBC Essentials: Team, State, Collaboration, Elastic Resources

DB workspace, spark, platform

Let us dive into Scala crash course in a notebook!

Scalable Data Science

prepared by Raazesh Sainudiin and Sivanand Sivaram

supported by and