Spark Online Training

About Spark Online Training:

Spark Online Training may be a unique framework for giant data analytics which provides one unique integrated API by developers for the aim of knowledge scientists and analysts to perform separate tasks. It supports a good range of popular languages like Python, R, SQL, Java and Scala. Apache Spark main aim is to supply hands-on experience to make real-time Data Stream Analysis and large-scale learning solutions for data scientists, data analysts and software developers.

About Instructor

                               Spark Online Training is provided by a real time consultant. The experience acquired by our trainer on Spark, is promisingly helpful to the corporate trainee’s. Our instructors are experts in the implementation and support projects. ASTS always works on real time scenarios. It is extremely useful for the professionals to handle the projects easily in the IT industry.

Prerequisites:

  • Basic knowledge of object-oriented programming is enough Knowledge of Scala will be an added advantage
  • Learners who have basic knowledge on Database, SQL Query will be an added advantage for Learning this Course

Terms And Conditions

  • We will Provide Supporting to resolve Student practical Issues.
  • We will provide server Access and 100% Lab Facility.
  • Resume Preparation.
  • Interview Questions & Answers.
  • We will conduct mock interviews. Student also gets 100% supporting before and after getting job.

Apache Spark Online Training Course Content

Batch and Real-Time Analytics with Apache Spark

SCALA (Object Oriented and Functional Programming)

  • Getting started With Scala
  • Scala Background, Scala Vs Java and Basics
  • Interactive Scala – REPL, data types, variables, expressions, simple functions
  • Running the program with Scala Compiler
  • Explore the type lattice and use type inference
  • Define Methods and Pattern Matching

Scala Environment Set up

  • Scala set up on Windows and UNIX

Functional Programming

  • What is Functional Programming?
  • Differences between OOPS and FPP

Collections ( Very Important for Spark )

  • Iterating, mapping, filtering, and counting
  • Regular expressions and matching with them
  • Maps, Sets, group By, Options, flatten, flat Map
  • Word count, IO operations, file access, flatMap

Object-Oriented Programming

  • Classes and Properties
  • Objects, Packaging, and Imports
  • Traits
  • Objects, classes, inheritance, Lists with multiple related types, apply

Integrations

  • What is SBT?
  • Integration of Scala in Eclipse IDE
  • Integration of SBT with Eclipse

SPARK CORE

  • Batch versus real-time data processing
  • Introduction to Spark, Spark versus Hadoop
  • The architecture of Spark
  • Coding Spark jobs in Scala
  • Exploring the Spark shell to  Creating Spark Context
  • RDD Programming
  • Operations on RDD
  • Transformations
  • Actions
  • Loading Data and Saving Data
  • Key Value Pair RDD
  • Broadcast variables

Persistence

  • Configuring and running the Spark cluster
  • Exploring to Multi-Node Spark Cluster
  • Cluster management
  • Submitting Spark jobs and running in the cluster mode
  • Developing Spark applications in Eclipse
  • Tuning and Debugging Spark

CASSANDRA ( N0SQL DATABASE )

  • Learning Cassandra
  • Getting started with architecture
  • Installing Cassandra
  • Communicating with Cassandra
  • Creating a database
  • Create a table
  • Inserting Data
  • Modelling Data
  • Creating an Application with Web
  • Updating and Deleting Data

Spark Integration with NoSQL (CASSANDRA) and Amazon EC2

  • Introduction to Spark and Cassandra Connectors
  • Spark With Cassandra  to Set up
  • Creating Spark Context to connect the Cassandra
  • Creating Spark RDD on the Cassandra Database
  • Performing Transformation and Actions on the Cassandra RDD
  • Running Spark Application in Eclipse to access the data in the Cassandra
  • Introduction to Amazon Web Services
  • Building 4 Node Spark Multi-Node Cluster in Amazon Web Services
  • Deploying in Production with Mesos and YARN

Spark Streaming

  • Introduction of Spark Streaming
  • Architecture of Spark Streaming
  • Processing Distributed Log Files in Real Time
  • Discretized streams RDD
  • Applying Transformations and Actions on Streaming Data
  • Integration with Flume and Kafka
  • Integration with Cassandra
  • Monitoring streaming jobs

Spark SQL

  • Introduction to Apache Spark SQL
  • The SQL context
  • Importing and saving data
  • Processing the Text files, JSON and Parquet Files
  • DataFrames
  • user-defined functions
  • Using Hive
  • Local Hive Metastore server

Spark MLLib

  • Introduction to Machine Learning
    Types of Machine Learning
  • Introduction to Apache Spark MLLib Algorithms
  • Machine Learning Data Types and working with MLLib
  • Regression and Classification Algorithms
  • Decision Trees in depth
  • Classification with SVM, Naive Bayes
  • Clustering with K-Means
  • Building the Spark server

 

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