Big Data certification course in Bangalore

 

Big Data certification course in Bangalore, Welcome to the land of India’s Leading E-Learning platform. We Prwatech the best big data architecture certification Pioneers offering advanced Big Data Hadoop Certification Training to all the Technology enthusiasts who are keen to brush up their skills and who wanted to become the Pro-certified Big Data Developer. We don’t end up Our Big Data Hadoop Certification Training in Bangalore just by providing the advanced big data developer certification; we help our students to understand all the modules of the Technology from 0 to Advanced like a Pro.

 

So one who taken Prwatech Best Big Data certification course in Bangalore from Prwatech can able to handle any kind of hurdles and challenges easily while working for any Top MNC companies. All the Certification Course modules are designed by 15+ years of Experienced Certified Professionals. So one chooses our Big Data certification course in Bangalore can get the chance to learn the Course under world-class Trainers with world-class classroom Environment. What are you waiting for? Are you? No Right, Cool then Enroll with us now and get All the Cool benefits of Prwatech.

 

Best Big data Certification Course Training

 

Our Trainers are capable to deliver the Best Real-time Experience to the students who are participating in our Big Data Certification Program. We designed Our Course with flexible timings which means one who wanted to learn the Technology can able to achieve it choosing the Comfortable time slots as per their comfortable timings. Along with that those who choose our programs can also get Free access to Our Dedicated YouTube Channel and Access to Prwatech LMS Platform.

What are the types of big data certifications?

 

  • Microsoft’s MCSE: Data Management and Analytics
  • Data Mining and Applications Graduate Certificate
  • SAS Certifications
  • Oracle Business Intelligence
  • IBM Certified Solutions Advisor
  • Hortonworks
  • Cloudera Certified Professional Data Engineer
  • EMC Data Scientist Associate (EMCDSA)

How to Get Big Data Certification?

 

So are you the one who wanted to brush-up your skills and become the Pro-certified Developer? Or the one who is eager to take the advanced big data analyst certification in Bangalore? Then Prwatech Big Data certification course in Bangalore is an ideal option for you.

Benefits of big data certification at Prwatech

 

  • 24*7 Supports
  • Support after completion of Course
  • Free Webinar Access
  • Online Training
  • Interview Preparation
  • Real Times Projects
  • Access to LMS
  • Access to Dedicated YouTube Channel

Who should do Big Data certification?

This Certification is

  • For Managers
  • For Java developers
  • For Testers
  • For Business analyst
  • For Project managers
  • For Beginners

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Prerequisites for Big data certification

Before you start proceeding with this tutorial, we will assume that you have some prior exposure to Core Java, database concepts, and Linux operating system flavors.

Syllabus for big data certification in Bangalore

Module 1: Hadoop Architecture

Learning Objective: In this module, you will understand what is Big Data, What are its limitations of the existing solutions for Big Data problem; How Hadoop solves the Big Data problem, What are the common Hadoop ecosystem components, Hadoop Architecture, HDFS and Map Reduce Framework, and Anatomy of File Write and Read.

Topics,

  • Hadoop Cluster Architecture
  • Hadoop Cluster Mods
  • Multi-Node Hadoop Cluster
  • A Typical Production Hadoop Cluster
  • Map Reduce Job execution
  • Common Hadoop Shell Commands
  • Data Loading Technique: Hadoop Copy Commands
  • Hadoop Project: Data Loading
  • Hadoop Cluster Architecture

Module 2: Hadoop Cluster Configuration and Data Loading

Learning Objective: In this module, you will learn the Hadoop Cluster Architecture and Setup, Important Configuration in Hadoop Cluster and Data Loading Techniques.

Topics,

  • Hadoop 2.x Cluster Architecture
    Federation and High Availability Architecture
  • Typical Production Hadoop Cluster
  • Hadoop Cluster Modes
  • Common Hadoop Shell Commands
  • Hadoop 2.x Configuration Files
  • Single Node Cluster & Multi-Node Cluster set up
  • Basic Hadoop Administration

Module 3: Hadoop Multiple node cluster and Architecture

Learning Objective: This module will help you understand multiple Hadoop server roles such as Name node & Data node, and Map Reduce data processing. You will also understand the Hadoop 1.0 cluster setup and configuration, steps in setting up Hadoop Clients using Hadoop 1.0, and important Hadoop configuration files and parameters.

Topics,

  • Hadoop Installation and Initial Configuration
  • Deploying Hadoop in the fully-distributed mode
  • Deploying a multi-node Hadoop cluster
  • Installing Hadoop Clients
  • Hadoop server roles and their usage
  • Rack Awareness
  • Anatomy of Write and Read
  • Replication Pipeline
  • Data Processing

Module 4: Backup, Monitoring, Recovery, and Maintenance

Learning Objective: In this module, you will understand all the regular Cluster Administration tasks such as adding and removing data nodes, name node recovery, configuring backup and recovery in Hadoop, Diagnosing the node failure in the cluster, Hadoop upgrade, etc.

Topics,

  • Setting up Hadoop Backup
  • White list and Blacklist data nodes in the cluster
  • Setup quotas, upgrade Hadoop cluster
  • Copy data across clusters using distcp
  • Diagnostics and Recovery
  • Cluster Maintenance
  • Configure rack awareness

Module 5: Flume (Dataset and Analysis)

Learning Objective: Flume is a standard, simple, robust, flexible, and extensible tool for data ingestion from various data producers (webservers) into Hadoop.

Topics,

  • What is Flume?
  • Why Flume
  • Importing Data using Flume
  • Twitter Data Analysis using hive

Module 6: PIG (Analytics using Pig) & PIG LATIN

Learning Objective: In this module, we will learn about analytics with PIG. About Pig Latin scripting, complex data type, different cases to work with PIG. Execution environments, operation & transformation.

Topics,

  • Execution Types
  • Grunt Shell
  • Pig Latin
  • Data Processing
  • Schema on reading Primitive data types and complex data types and complex data types
    Tuples Schema
  • BAG Schema and MAP Schema
  • Loading and storing
  • Validations in PIG, Typecasting in PIG
  • Filtering, Grouping & Joining, Debugging commands (Illustrate and Explain)
    Working with function
  • Types of JOINS in pig and Replicated join in detail
  • SPLITS and Multi query execution
  • Error Handling
  • FLATTEN and ORDER BY parameter
  • Nested for each
  • How to LOAD and WRITE JSON data from PIG
  • Piggy Bank
  • Hands-on exercise

Module 7: Sqoop (Real-world dataset and analysis)

Learning Objective: This module will cover Import & Export Data from RDBMS (MySql, Oracle) to HDFS & Vice Versa

Topics,

  • What is Sqoop
  • Why Sqoop
  • Importing and exporting data using sqoop
  • Provisioning Hive Metastore
  • Populating HBase tables
  • SqoopConnectors
  • What are the features of the scoop
  • Multiple cases with HBase using client
  • What are the performance benchmarks in our cluster for the scoop

 

Module 8: HBase and Zookeeper

Learning Objectives: This module will cover advance HBase concepts. You will also learn what Zookeeper is all about, how I help in monitoring a cluster, why HBase uses zookerper and how to build an application with zookeeper.

 

Topics,

  • The Zookeeper Service: Data Model
  • Operations
  • Implementations
  • Consistency
  • Sessions
  • States

 

Module 9: Hadoop 2.0, YARN, MRv2

Learning Objective: in this module, you will understand the newly added features in Hadoop 2.0, namely MRv2, Name node High Availability, HDFS Federation, and support for Windows, etc.

 

Topics,

  • Hadoop 2.0 New Feature: Name Node High Availability
  • HDFS Federation
  • MRv2
  • YARN
  • Running MRv1 in YARN
  • Upgrade your existing MRv1 to MRv2

Module 10: Map-Reduce Basics and Implementation

This module, will work on Map-Reduce Framework. How Map Reduce implements on Data which is stored in HDFS. Know about input split, input format & output format. Overall Map Reduce process & different stages to process the data.

 

Topics

  • Map Reduce Concepts
  • Mapper Reducer
  • Driver
  • Record Reader
  • Input Split (Input Format (Input Split and Records, Text Input, Binary Input, Multiple Input
  • Overview of InputFileFormat
  • Hadoop Project: Map-Reduce Programming

 

Module 11: Hive and HiveQL

In this module, we will discuss a data warehouse package that analysis structure data. About Hive installation and loading data. Storing Data in different tables.

 

Topics,

  • Hive Services and Hive Shell
  • Hive Server and Hive Web Interface (HWI)
  • Meta Store
  • Hive QL
  • OLTP vs. OLAP
  • Working with Tables
  • Primitive data types and complex data types
  • Working with Partitions
  • User-Defined Functions
  • Hive Bucketed Table and Sampling
  • External partitioned tables, Map the data to the partition in the table
  • Writing the output of one query to another table, multiple inserts
  • Differences between ORDER BY, DISTRIBUTE BY and SORT BY
  • Bucketing and Sorted Bucketing with Dynamic
  • RC File, ORC, SerDe: Regex
  • MAPSIDE JOINS
  • INDEXES and VIEWS
  • Compression on Hive table and Migrating Hive Table
  • How to enable update in HIVE
  • Log Analysis on Hive
  • Access HBase tables using Hive
  • Hands-on Exercise

 

Module 12: Oozie

Learning Objective: Apache Oozie is the tool in which all sorts of programs can be pipelined in the desired order to work in Hadoop’s distributed environment. Oozie also provides a mechanism to run the job at a given schedule.

 

Topics:

  • What is Oozie?
  • Architecture
  • Kinds of Oozie Jobs
  • Configuration Oozie Workflow
  • Developing & Running an Oozie Workflow (Map Reduce, Hive, Pig, Sqoop)
  • Kinds of Nodes

 

Module 13: Spark

Learning Objectives: This module includes Apache Spark Architecture, How to use Spark with Scala and How to deploy Spark projects to the cloud Machine Learning with Spark. Spark is a unique framework for big data analytics which gives one unique integrated API by developers for the purpose of data scientists and analysts to perform separate tasks.

 

Topics,

  • Spark Introduction
  • Architecture
  • Functional Programming
  • Collections
  • Spark Streaming
  • Spark SQL
  • Spark MLLib

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35 Hours
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15 Seats
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