PySpark : Python Spark Hands On Professional Training : 2019

PySpark Structured Streaming (Using Python) : Professional Training with Hands On Sessions :

In total 22+ Modules and 8+ Hrs
Hands On Exercises
Core concepts and fundamentals are covered
Each Video with Hand Written PDFs and cover important concepts

Video Demo Sessions

To process the real-time or near real-time data, there are lots of engine/solutions are available. And each of them has some issues like they don’t support late data processing, they are highly complex to setup, complex programming API etc. Yes, writing streaming solution either framework provider has to take a pain and make it easy for developer/user. Or developer has to take a pain to implement correctly. In case of Spark, the previous solution was Dstream, Spark team had provided a framework which work on RDD, for writing streaming solution. And in this case developer/user has to take pain and implement complex solution. However, Spark team realizes it and they decided to write entire streaming solution from scratch. And the outcome of this is Structured Streaming, which has simple API and performance optimization taken care by the SparkSQL engine.

It seems Structured Streaming is simple to learn, but answer is no. You need to know, many concepts as well as real hands on exercises. How the streaming works. Yes, API is quite simple but until you do the hands on, you would not realize. Why things are not working or they are working in the background or not. Is this generating result for what you have written the program or not. There are so many things. Hence, HadoopExam.com bring this new training course which cover important concepts from basic to advanced, and provide many hands on exercise to learn fast growing Spark Structured Streaming solution. Same way easy learning process… check the syllabus below.

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In this training we are focusing on both fundamental concepts as well as Hands on Exercises. We start with the concepts, and than set up the environment using Ubuntu Linux (On Window’s VMWare), same instructions you can follow for Mac OS as well. Once we created the environment we will be covering many Hands On Exercises, which will make you expert for the PySpark Structured Streaming. As of now total training length is 8+ Hours. You can watch the below demo sessions as well to check the quality of the training. As you know, Spark 2.x certification also released by Databricks and they have separate certification material for the Python professional.

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Syllabus Covered as Part of This training (Become an Spark Structured Streaming Expert in around 8+ hours training) : 10 Hands On Exercises Covering all the concepts

Module-1: Spark Streaming in Depth Part-1 ( PDF Download & Available Length 26 Minutes)

Real/Near real time data processin
Streaming Sources and Sinks
Streaming Concepts
Stock Visualization Example (How Streaming Helpful)

Module-2: Apache Spark Introduction DataFrame ( PDF Download & Available Length 21 Minutes)

DataFrame
DataFrame v/s Dataset
Sample API for DataFrame
Language Independent Catalyst Optimizer

Module-3: Introduction Apache Spark Catalyst optimizer ( PDF Download & Available Length 38 Minutes)

What is Catalyst optimizer
Concepts of Tree and Rules
Various Phases of Catalyst optimizer
Analysis
Logical optimization
Physical planning
Code Generation
Predicate Pushdown
Constant Folding
Physical operator
Project Prunning

Module-4 : Introduction of Structured Streaming ( PDF Download & Available Length 38 Minutes)

Purpose : How it differs from other Streaming Solutions
Anatomy of Structured Streaming Application

Source
Input Table
Transformation
Result Table
Sink

Catalyst Optimization of Streaming Application

Module-5 : Programming concepts of Structured Streaming ( PDF Download & Available Length 11 Minutes)

Programming and Basic Concepts of Structured Streaming
Discussion with Pseudo Code
Some important points

Module-6 : Structured Streaming is different and less painful ( PDF Download & Available Length 19 Minutes)

Event Time v/s Processing Time
Understanding of Late Processing
Exactly once processing
Re-playable sources and Idempotent sink

Module-7: Structured Streaming Essentials ( PDF Download & Available Length 16 Minutes)

Common Issues with Legacy DStream solution
Essentials for Structured Streaming
Introduction to Triggers
Introduction to Watermarks
Revisit the concepts Learned
Assumptions for Structured Streaming

Module-8A : Install VMWARE Workstation Player ( PDF Download & Available Length 8 Minutes) : Env Setup for Hands On

Module-8B : Install Ubuntu Linux in VMWare Player ( PDF Download & Available Length 23 Minutes) : Env Setup for Hands On

Install Ubuntu Image
Install SSH server
Install Putty and connect to Linux OS

Module-8C : Install Apache Spark ( PDF Download & Available Length 17 Minutes) : Env Setup for Hands On

Install Apache Spark
Start spark-shell
Start pyspark

Module-9 : Update Spark Installed version ( PDF Download & Available Length 8 Minutes) : : Env Setup for Hands On

Install latest version of Spark

Module-10 : Sample Streaming Exercise ( PDF Download & Available Length 9 Minutes) : Hands On (1-Exercise)

Module-11 : Sample Streaming Exercise ( PDF Download & Available Length 20 Minutes) : Hands On (3-Exercises)

Reading from a Directory and Display on the console
Reading from a Directory and use SQL query operations
Aggregation query

Module-12: Late Event and Watermark ( PDF Download & Available Length 30 Minutes)

Late Data and Watermarks
Common operations on Streaming Data
Understanding of Window Operations
Window and Group By Operations

Module-13: Output Modes ( PDF Download & Available Length 25 Minutes)

Append Mode
Append Mode and Watermarks
Append Mode and Aggregations
Append Modes Guaranteed Data Processing

Complete Mode
Complete Modes and Triggers
Complete Mode + Watermark + Aggregations

Update Modes
Update Mode and Watermark

Module-14: Process JSON data & Output as a Parquet file ( PDF Download & Available Length 21 Minutes) : Hands On : (1-Exercise)

Module-15: Watermark and Output modes Hands on Exercise ( PDF Download & Available Length 59 Minutes) : Hands On (1-Big Exercise, covering 5 concepts)

Module-16: Window and multi-key aggregations Hands On Exercise ( PDF Download & Available Length 20 Minutes) : Hands On (1-Exercise)

Module-17: Processing JSON Data ( PDF Download & Available Length 30 Minutes) : Hands On (1-Big Exercise, covering 2 concepts)

Json Data processing
File Triggers
Memory sink
Stream status
Static Data Join with Stream

Module-18: Inner and Outer Joins ( PDF Download & Available Length 33 Minutes)

Stream-static joins
Stream-Stream join challenges
Inner Join
Inner Join and watermark
Outer Join and Watermark
Outer Join Important points

Module-19: Inner and Outer Joins ( PDF Download & Available Length 20 Minutes) : Hands On (1-Big Exercise, covering 2 concepts)

Stream-static joins
Stream-Stream join challenges
Inner Join
Inner Join and watermark
Outer Join and Watermark
Outer Join Important points

Module-20: Drop Duplicate data ( PDF Download & Available Length 11 Minutes)

Remove duplicate data (unbounded)
Remove duplicate data (bounded)

Module-21: Drop Duplicate data ( PDF Download & Available Length 11 Minutes) : Hands On : (1-Exercise)

Remove duplicate data (unbounded)
Remove duplicate data (bounded)

Module-22: Structured Streaming: Multiple Streams ( PDF Download & Available Length 11 Minutes)

Global Watermark
Foreach and Foreachbatch
Triggers
One Time Batch (Cost saving optimization)
Monitoring operations on Structured Streaming

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