// 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 
:
Outline
I. 21 Easy Steps for Sharing your AWS Educate Credits
- Workspace -> scalable-data-science -> xtraResources -> awsEducate -> sharing (relative to ‘Workspace’ link!)
- If you are not in
*.cloud.databricksor the above link is useless then go to html here.
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?
DBC Essentials: Shard, Cluster, Notebook and Dashboard
DBC Essentials: Team, State, Collaboration, Elastic Resources
Let us dive into Scala crash course in a notebook!
Scalable Data Science
prepared by Raazesh Sainudiin and Sivanand Sivaram
supported by
and