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PySpark 2.X (Using Python) Professional Training with Hands On Sessions :
- In total 21+ Modules and 8+ Hrs
- Hands On Exercises
- Core concepts and fundamentals are covered
- Each Video with Hand Written PDFs and cover important concepts
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Python is Must language for Data Scientists, Business Analytics and Data Engineer. Hence, Apache has to develop and support there most active project in the Python Progamming language as well. And so the PySpark is developed. All the professionals who are working on the Python programming language do not have to learn a new Programming Language to work with the most active framework. We have been rceiving lot many request for the PySpark training, because of our most successful Spark training in Scala. We decided to work on the PySpark as well and provide even more better quality training in PySpark. Hence, we are very happy to provide this PySpark core training to all our professionals who were waiting for this training to be launched. Hence, if you are already working in the Python then dont miss this training, this is a must learn technology for you. We provide training in such a easy way as well as equally having the focus on core concept as well. Please subscribe this training now... If you are looking annual subscription for all the trainings and books available with the HadoopExam.com then visit this page (Annual Subscription). We have more 50000 subscriber across all our product which proves the quality of the material. This training is most usful for following professional
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Most subscribed Annual Package (This training is Part of our Annual Subscription as well)
Syllabus Covered as Part of This training (Become an Spark 2.x SQL Expert in around 8+ hours training)
Module-1:
Apache Spark Introduction (PDF Download & Available Length 22 Minutes)
Module-5B : Install Ubuntu Linux in VMWare Player ( PDF Download & Available Length 23 Minutes & HandsOn )
Module-18B: Read and Write Data + File Formats (PDF Download & Available Length 23 Minutes & HandsOn )
- Spark v/s MapReduce
- Why Hadoop to be used?
- HDFS
and YARN Intro
- Introduction to Iterative algorithm
- Multiple Reasons behind Spark High Performance
- RDD :
Native Spark API intro
- Cluster : Group of Computers
- Spark Application Components
- Driver
- Executors
- Cluster Manager
- SparkSession
- Transformation
- Lazy Evaluation
- Narrow Transformation
- Wide
Transformation
- DataFrame
- DataFrame v/s Dataset
- Sample API for DataFrame
- Language
Independent Catalyst Optimizer
Module-5B : 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
- Install Apache Spark
- Start spark-shell
- Start pyspark
- About RDD
- RDD V/s DataFrame v/s Dataset
- RDD and
Custom Partitioner concept
- 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
- Direct Acyclic Graph
- DataFrame v/s Dataset
- Explicit Schema for DataFrame/Dataset
- Columns in DataFrame
- Execution Path and Execution steps
- Runtime
Optimizations
- Schema, StructType and StructFields
- Manual Schema Assignment
- Creating
and selecting columns
- Creating Rows
- expr and selectExpr
- Basics of Literals
- Hands on Exercise
- Unique Rows
- Explicit Assign Schema
- Working with columns and Rows
- Sorting Data
- Union of Rows
- Limit
- Repartition and Coalesce
- Collecting
Rows on the Driver
- Spark has its own DataTypes
- Boolean Expression (True/False)
- Serially Define the filter
- Working
with Numerical Data
- Works with Character Data
- Using Regular Expression
- Dates
and Timestamp
- Struct Data Type
- Array Data Types
- Explode Example
- Map Types
- User
Defined Function
- DataFrame and GroupBy operation
- Understanding RelationalGroupedDataset
- Basic Aggregation Operation
- Working
with Complex DataTypes
- Understand Window Function
- Grouping Set function
- Pivot
- Cubes
- Inner Join
- Left Outer Join
- Right Outer Join
- Full
Outer Join
- Left Semi Join
- Left Anti Join
- Shuffle Join
- Broadcast
Join
Module-18B: Read and Write Data + File Formats (PDF Download & Available Length 23 Minutes & HandsOn )
- Understanding with the DataFrameReader
- Various Data Read Modes
- Permissive , Drop malformed , FailFast
- Working with the DataFrameWriter
- Save Modes
- Append , Overwrite , Ignore , errorIfExists
- HandsOn Exercises
- String, Date and Timestamp
- Working with Fields separator
- Generating and working with the file formats (Read and Write as well)
- ORC File
- Parquet File
- Json
- Csv
- Text
- Spark Driver Process
- Spark Executors
- Cluster Manager
- Various Execution Modes
- Cluster Mode
- Client Mode
- Local
Mode
- Submit application and its Flow
- Spark App Understanding in Depth
- Application, Job, Stage and Task
- Spark Shuffle
- Tasks
-
Pipeline
- What is Partitioning and why?
- Data Partitioning example using Join (Hash Partitioning)
- Understand Partitioning using Example for get Recommendations for Customer
- Understand Partitioning code using Spark-Scala
- Operations which create Partitioned RDD
- Operation which get benefit of Partitioning
- Operation
that affect the partitioning
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