Spark With Scala- Weekend Course - 6 Hours a Day

Overview

Apache Spark with Scala training will advance your expertise in Distributed programming with Spark and Scala. Skill set gained through the course in Core Spark, Scala, SparkSQL and Streaming will help you to solve complex problem. Deep knowledge of Spark with Scala will always make you distinct, which will open a successful path for your career.

 Objective

Hadoop mapreduce faciliteted to solve complex problems on distributed systems but with some limitations. This course will discuss limitation of Hadoop mapreduce and how Spark overcomes those limitations. We describe RDDs which is core of Spark and In memory computation. Understanding of persistent RDDs, in memory computation, and solving Big Data problems using Spark with Scala is core of this course. Discussion will move through SparkSQL and problem solving with SparkSQL dataframes. Hand-on is the parallel movement for all the discussion. Concept on dealing with streaming data with Spark  Streaming is also an important topic, which is included. Last part of course is Spark program optimization. Optimization of Spark core, Spark SQL,  Spark streaming and optimizing the utilization of cluster system . We discuss Spark on Yarn, Standalone and Mesos cluster too.

Detail Description of class :

Introduction to Big Data and Distributed Computing :

 Big data analysis is future. This section of course will help you to understand, the need of distributed computation.

• Introduction to data.
• Data Science a vision.
• Big data Introduction.
• Parallel computation.
• Problem with parallel computation.
• Traditional parallel computation systems.

Hadoop :

• Introduction to Hadoop.
• Hadoop Components.
• HDFS and its architecture.
• HDFS Commands

◦     mkdir

◦     ls

◦     rmdir and rm

◦     copyFromLocal

◦     put

◦     cat

◦     copyToLocal

◦     get

◦     touchz

◦     mv

◦     cp

◦     distcp

◦     etc…...

• fsimage and edits log files.
• Hadoop property files.
• Introduction to MapReduce.
• Shortcoming of MapReduce.

 Scala :

• Introduction to Scala
• Scala variables
• Operators in Scala
• Interactive mode and script base programming introduction
• Scala data type and operations on them
• Scala Collections (Touple, Map etc)
• Control Flow and looping in Scala
• Functions in Scala (Declaration, Definition Types and calling)
• Object oriented Scala
• Introduction to function programming in scala.
• Pattern Matching a introduction.

 Spark Introduction :

• Introduction to Spark.
• Spark and Hadoop (Similarity and Differences)
• Spark Execution (Master Slave System , Drive, Driver manager and Executors)
• Spark Shell
• Resilient Distributed dataSet (RDD)

Operations On RDD :

• Creation of RDD
• Transformation and Action Introduction
• Lazy evaluation
• Some Important Transformation :
• filter
• map
• flatMap
• distinct
• sample
• union
• intersection
• subtract
• cartesian
• Some Important Action
• first
• take
• top
• reduce
• fold
• aggregate
• foreach
• count
• collect
• Creation of Paired RDD
• Some important Transformation on pairRDD
• combineBy
• mapValues
• groupByKeys
• reduceByKeys
• sortByKeys
• subsractByKey
• Joines and their Type
• cogroup
• Some Important action on pair RDD
• lookUp
• collectAsMap
• countByKey
• Hands on all the functions

Fault tolerance and Persistence :

• RDD lineage
• persistence
• Benefit of persistence

 Optimizing Spark program

• Introduction to partitioning
• Inbuilt partitioners (Hash and Range)
• Benefits of partitioning
• groupByKey and reduceBykey comparison
• Spark broadcasting and accumulators

IO in Spark :

• TextFile
• Csv File
• JSON
• Data From HDFS

Spark Streaming :

• Introduction to Spark Streaming
• Transformation
• Reading from HDFS
• Window Concept
• Push Based Receiver and Pull Based receiver
• Kafka integration with Streaming.
• Performance

• Introduction to SparkSQL
• SparkSQL datatype
• DataFrame an Introduction.
• Creation of a dataframe.
• Summary statistics on DataFrame.
• Aggregation  on Given Data.
• SparkSQL and SQL
• Introduction to Hive.
• Using data from Hive and HiveQL.
• Optimizing SparkSQL code.

Spark Code Deployment and cluster managers.

• Submitting Spark  code on StandAlone cluster manager.
• Submitting Spark  code on  YARN
• Submitting Spark code on Mesos

Note  : Every part of course will be associated with hands on . A number of objective questions will always help you in scratch your brain.

 Projects :

 Project 1 : Spark core can be used for data preparation and  aggregation. Aggregation will be implemented using Spark core APIs.

For data aggregation movie lance data will be used.

 Project 2 :  Implementing streaming data word frequency visualization.  using Kafka and Spark streaming integration.

 Project 3 : Implementation of Moving average using SparkSQL.

 Project 4 : Data preprocessing, data manipulation and aggregation using SparkSQL.  It will  be done using Real time

For any queries reach out to info@walsoul.com or +919900498065

Instructor - Raju Mishra (Linkedin - https://www.linkedin.com/in/raju-mishra-40322a104/

Detailed Course Content - Walsoul.com

Note:  This course is both online as well as offline, offline classroom classes will be conducted at BTM Bangalore