The document outlines a hands-on workshop on data processing using PySpark with Python, hosted by Itversity, which includes a recap of Python basics and an introduction to Spark. Attendees will learn to create Resilient Distributed Datasets (RDDs), read from various file formats, and perform standard transformations and actions. The workshop provides access to labs and requires prior knowledge of programming in Python or Scala.

1. Data Processing using
pyspark
Spark using Python as programming language

2. About us
 ITVersity is a holistic continuous learning platform using
 Video content – www.YouTube.com/itversityin
 Subscription based labs – https://labs.itversity.com
 Community based support – http://discuss.itversity.com
 Udemy: https://www.udemy.com/user/it-versity/
 20$ Coupon – pycon2017 (100 coupons will be issued at the time of
workshop)

3. Agenda
 Hands on workshop
 Revise Python
 Understand Spark APIs
 Develop programs to process data

4. Objectives
 Data Model
 Revising Python
 Introduction to Spark
 Initializing the job
 Create RDD using data from HDFS
 Read data from different file formats
 StandardTransformations – using core API
 StandardTransformations – using Spark SQL
 Saving RDD back to HDFS
 Save data in different file formats

5. LabAccess
 1 day lab access will be provided for the workshop audience
 Credentials will be shared at the time of workshop
 Make sure to install putty or cygwin for accessing the lab
 32 bit OS machines will not be supported

6. Data Model -
retail_db

7. Revising
Python
 Quick recap of important concepts in Python
 Basic programming constructs
 Lambda Functions
 Basic I/O operations
 Collections and Operations on collections
 Get revenue for given order_id, by adding order_item_subtotal
from order_items
 Read data from local file system /data/retail_db/order_items/part-
00000

8. Introduction to
Spark
 Spark is Distributed computing framework
 Bunch ofAPIs to process data
 Higher level modules such as Data Frames/SQL, Streaming, MLLib
and more
 Well integrated with Python, Scala, Java etc
 Spark uses HDFS API to deal with file system
 It can run against any distributed or cloud file systems – HDFS, s3,
Azure Blob etc
 Only Core Spark and Spark SQL (including Data Frames) is part of
the curriculum for CCA Spark and Hadoop Developer. CCA also
requires some knowledge of Spark Streaming.
 Pre-requisites – Programming Language (Scala or Python)

9. Introduction to
Spark -
Documentation
 Use 1.6.x documentation for now
 Get familiar with documentation as much as possible

10. Initializing the
job
 Initialize using pyspark
 Running in yarn mode (client or cluster mode)
 Setting up additional properties – spark.ui.port
 Programmatic initalization of job
 Create configuration object
 Create spark context object

11. Create RDD
using data
from HDFS
 RDD – Resilient Distributed Dataset
 In-memory
 Distributed
 Resilient
 Reading files from HDFS
 Reading files from local file system and create RDD
 Quick overview ofTransformations andActions
 DAG and lazy evaluation
 Previewing the data using Actions

12. Read data
from Different
file formats
 Data Frame (Distributed collection with structure)
 APIs provided by sqlContext
 Supported file format
 orc
 json
 parquet
 avro
 Previewing the data
 Show
 Let us read data from json files /public/retail_db_json/orders

13. Standard
Transformatio
ns
 Standard transformations
 String Manipulation (Python)
 Row level transformations
 Filtering (horizontal and vertical)
 Joins
 Aggregation
 Sorting
 Ranking
 Set Operations

14. Standard
Transformatio
ns
 Writing SQL for standard transformations
 String Manipulation
 Row level transformations
 Filtering (horizontal and vertical)
 Joins
 Aggregation
 Sorting
 Ranking
 Set Operations

15. Saving RDD
back to HDFS
 Make sure data is saved with proper delimiters

16. Save data in
different file
formats
 Data Frames can be saved into different file formats
 json
 orc
 parquet