Spark SQL Professional Training with Hands On Sessions , 2018 Spark Certification

Spark 2.X SQL (Using Scala) Professional Training with Hands On Sessions :

In total 19 Modules 22 Videos and 8+ Hrs coreect
37 Hands On Exercises
Core concepts and fundamentals are covered
Each Video with Hand Written PDFs

Video Demo Sessions

Concepts

Environment Creation

Hands On Exercises

Since last couple of years it was found that Spark is one of the fastest growing technology and demand for Spark professionals has increased exponentially. At the same time HadoopExam also providing best material for Spark. Hence, we have released this Spark SQL professional training with the latest available Spark platform. Spark SQL is must for all the below professionals as below

Developer
Data Engineer
Data Analytics
Data Scientists

If you are working in any of the above domain than you can not skip Spark SQL. Even, Spark team has also putting lot of effort on Spark SQL. All the new modules e.g. GraphFrame, Machine Learning and Structured streaming are being developed using Spark SQL. And one of the two important technology and architecture changes which are part of Spark SQL are below and helping Spark in improving Spark performance drastically.

Catalyst Optimizer (Heart of Spark SQL)
Project Tungsten (Bring Spark as close to Bare Metal as possible)

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 almost 37 various Hands On Exercises, which will make you expert for the Spark SQL As of now total training length is 8+ Hours. You can watch the above demo sessions as well to check the quality of the training. As you know, Spark 2.x certification also released by Databricks and Spark SQL is one of the major topic for that certification exam.

Below is the best combo for this certification (Pack3TRNDBSparkSQL2Scala)

1. Apache Spark (Scala) Professional Training (Core Saprk and Fundamentals)

2. Spark SQL (Scala) Professional Training with Hands On

3. Databricks Certified Developer Apache Spark 2.x for Scala (Cert No : PR000003)

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Syllabus Covered as Part of This training (Become an Spark 2.x SQL Expert in around 8+ hours training)

Module-1 : Introduction to Spark SQL ( PDF Download & Available Length 37 Minutes)

What is new in Spark SQL
What are the main Goals of SparkSQL
Start of Dataset/DataFrame API
Data Pipelines
Introduction to UDF

Module-2 : Introduction Apache Spark SQL's 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

Scala Features concepts

Predicate Pushdown
Constant Folding
Physical operator
Project Prunning

Module-3 : Introduction to Project Tungsten ( PDF Download & Available Length 31 Minutes)

Purpose of project Tungsten
Binary Processing
Cache Aware Computation
Code Generation
Custom Memory Management

Module-4 : Create Spark Environment using Ubuntu Linux on Windows

Module-4A : Step by Step Installation of VMWARE Workstation Player ( PDF Download & Available Length 8 Minutes & HandsOn )

Module-4B : Install Ubuntu Linux in VMWare Player ( PDF Download & Available Length 23 Minutes & HandsOn )

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

Module-4C : Install Apache Spark ( PDF Download & Available Length 17 Minutes & HandsOn )

Install Apache Spark
Start spark-shell
Start pyspark

Module-5A : DataFrame and Dataset API Introduction ( PDF Download & Available Length 19 Minutes & Concepts )

DataFrame Introduction
Dataset Introduction
SparkSQL with DataFrame and Dataset

Module-5B : Exercise using RDD and DataFrame/Dataset API (PDF Download & Available Length 14 Minutes & HandsOn )

Module-6 : Json Dataset Exercise and Explanation step by step ( PDF Download & Available Length 21 Minutes & HandsOn )

Support for Hive Query Exercise
Loading JSON data
Explicitly assign Schema to Json Data
Working with loaded Json data

Module-7 : Spark SQL Encoders in Details ( PDF Download & Available Length 18 Minutes & Concepts )

Implicit Objects
Encoders (Special ser-de for SparkSQL)
Why Encoders are fast
Creating custom Encoders

Module-8 : Encoders Exercise ( PDF Download & Available Length 8 Minutes & HandsOn )

Module-9 : DataFrame and Dataset in Depth ( PDF Download & Available Length 18 Minutes & Concepts )

Dataset operation variants
Dataset and compile time check
Dataset transient values
Converting DataFrame to Dataset
Dataset Using Case classes
Dataset using Programmatic Schema

Module-10 : Dataset And DataFrame Exercises ( PDF Download & Available Length 18 Minutes & HandsOn )

Dataset API in three different formats
Go through Explain Plans
Dataset methods
Work with DataFrames

Module-11 : Apache SparkSQL Schema creation and understanding ( PDF Download & Available Length 24 Minutes & HandsOn )

SparkSQL StructType and StructField
Inferring Schema
Printing Schema in various ways
Nested StructType
Row object and accessing fields

Module-12A : DataFrameReader and DataFrameWriter ( PDF Download & Available Length 24 Minutes & HandsOn )

RowObject and RowSchema
RowEncoder
DataFrameReader Interface
DataFrameWriter Interface
Schema inference and compression
Dataset using Programmatic Schema

Module-12B : Apache SparkSQL Schema creation and understanding ( PDF Download & Available Length 28 Minutes & HandsOn )

RowObject and RowSchema
RowEncoder
DataFrameReader Interface
DataFrameWriter Interface

Module-13 : Dataset Caching and Checkpointing ( PDF Download & Available Length 30 Minutes & HandsOn )

Dataset Caching
Dataset Un-persisting
Dataset Eager checkpoint
Dataset Non-Eager checkpoint
Dataset Lineage truncation
Dataset Performance Improvements
DataFrame Caching
DataFrame Un-persist
Check the UI for storage

Module-14 : Dataset JOINS ( PDF Download & Available Length 25 Minutes & HandsOn )

Broadcast joins
Dataset joins
Dataset Joins and Hints

Module-15 : RelationalGroupedDataset ( PDF Download & Available Length 29 Minutes & HandsOn )

GroupBy operations on Datasets
Different types of aggregate functions
Dataset Union function
Grouping Sets example

Module-16 : SparkSQL Rollup, Cube and Pivot Operations ( PDF Download & Available Length 20 Minutes & HandsOn )

Rollup operations
Pivot Operations
Cube operations

Module-17 : Spark SQL Functions ( PDF Download & Available Length 20 Minutes & HandsOn )

Standard available functions
User defined functions (UDF)
Window Aggregate Function
Inline and Explicitly creating UDF
Understand UDF and Aggregate UDF functions
Define and Register UDF Functions
Define Aggregate UDF functions
Use custom created UDF Functions

Module-18 : SparkSQL Rank and Cumulative Distribution functions ( PDF Download & Available Length 12 Minutes & HandsOn )

Understanding and Hands on With Rank functions
Creating Windows Spec
Calculating Cumulative Distribution Values using CDF

Module-19 : Row Number and Lead-Lag window functions ( PDF Download & Available Length 12 Minutes & HandsOn )

Understanding of Row number functions
Understanding of Lead-Lag functions

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