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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
Access Training from this Link

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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