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1
Introduction
2
Background
3
Scaleout
4
DataFrame API
5
RDD vs DataFrame
6
PySpark API
7
PySpark
8
Scaling out ML
9
Notebook setup
10
Importing data
11
Python vs SQL
12
Creating persistent tables
13
Renaming columns
14
Pandas
15
Display
16
DropN
17
Exploring the Data
18
Persistence
19
Visualizations
20
More Data
21
Case Statements
22
Spark Sequel
23
Matplotlib
24
Adding a new column
25
Adding a new dataframe
26
Userdefined functions
27
Local Python dataframe
28
ML Live
29
Building the Model
30
Writing the Model
Description:
Explore Python on Spark with PySpark in Azure Databricks through this comprehensive 52-minute tutorial. Dive into basic concepts and witness extensive demonstrations in a Databricks notebook. Learn about scaleout, DataFrame API, RDD vs DataFrame, PySpark API, and scaling out ML. Follow along with notebook setup, data importing, Python vs SQL comparisons, and creating persistent tables. Master techniques for renaming columns, using Pandas, exploring data, persistence, and visualizations. Delve into case statements, Spark Sequel, Matplotlib, and user-defined functions. Conclude with hands-on experience in building and writing ML models. Access the accompanying notebook on GitHub for a complete learning experience.

Azure Databricks Using Python With PySpark

Bryan Cafferky
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