Big Data Hadoop Training in Nagpur

Big Data Hadoop  Training in Nagpur Get hands-on training in Hadoop and its ecosystem

Big Data Hadoop Training in Nagpur presently Big Data is a reality. the volume, diversity, and speed of information coming into associations keep on achieving remarkable levels. The Big Data appearance has risen because of tremendous measures of information that are getting to be accessible over an extensive variety of utilization spaces and enterprises


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Big Data Hadoop Training in Nagpur, with Real-time Practice and Live Projects

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Big Data Hadoop Training in Nagpur course overview

Hadoop is the flagship inside the Big Data ecosystem, containing a large group of various technologies. While there are numerous options and variations, including Cloudera, Hortonworks, Amazon EMR, HDInsight, Storm, and Apache Spark—Hadoop all in all remains the most-deployed Big Data innovation. From a career development point of view, the uplifting news for trying big data professionals is that regardless of flooding request, Hadoop’s reception is being defeated by the absence of accessible ability. Alone. The industry needs a crisp ability that has aced MapReduce, Impala, Pig, and different technologies based on Hadoop. Appzmine, which additionally runs a counselling marketplace, has a profound understanding of what the industry demands and what is needed for you to be effective when you complete the Big Data track. The upside of preparing with Appzmine is that our experts are industry thought leaders and professionals who are keeping pace with the fast-changing landscape of big data technologies. Because of the industry association of our experts, Appzmine big data Hadoop training is hands-on, practical and gives you an understanding of how big data tools and technologies are implemented in reality.

Prerequisites of Big Data Hadoop Training in Nagpur

Big Data Hadoop Training in Nagpur to get the most out of the training class, you need to be familiar with Linux file systems, Linux command line interface (CLI) and the basic Linux commands such as cd, ls, cp, etc. You also need to have basic programming skills in Python, and are comfortable with functional programming style, for example, how to use map() function to split a list of strings into a nested list. Object-oriented programming (OOP) in python is not required

Training from professional Big Data Hadoop expert

Appzmine developer has been implementing professional Analaytic solutions across a range of organization for many years. Those consultants write and teach our Big Data Hadoop training in Nagpur courses, so their experience directly informs course content.

Real-time Practice and Live Projects

Our Big Data Hadoop is designed to get you started in that simplify the handling of large amounts of data. We work on industry related projects in this Big Data Hadoop learning These projects help you create your Big Data Hadoop portfolio. You will work on the most high priority projects to help you get hands-on experience of how enterprises are deploying Hadoop in mission-critical applications.
Big Data Hadoop Training in Nagpur Course Content
Introduction to Big Data
  • Rise of Big Data
  • Compare Hadoop vs traditonal systems
  • Hadoop Master-Slave Architecture
  • Understanding HDFS Architecture
  • NameNode, DataNode, Secondary Node
  • Learn about JobTracker, TaskTracker
HDFS and MapReduce Architecture
  • Core components of Hadoop
  • Understanding Hadoop Master-Slave Architecture
  • Learn about NameNode, DataNode, Secondary Node
  • Understanding HDFS Architecture
  • Anatomy of Read and Write data on HDFS
  • MapReduce Architecture Flow
  • JobTracker and TaskTracker
Hadoop Configuration
  • Hadoop Modes
  • Hadoop Terminal Commands
  • Cluster Configuration
  • Web Ports
  • Hadoop Configuration Files
  • Reporting, Recovery
  • MapReduce in Action
Understanding Hadoop MapReduce Framework
  • Overview of the MapReduce Framework
  • Use cases of MapReduce
  • MapReduce Architecture
  • Anatomy of MapReduce Program
  • Mapper/Reducer Class, Driver code
  • Understand Combiner and Partitioner
Advance MapReduce - case A
  • Write your own Partitioner
  • Writing Map and Reduce in Python
  • Map side/Reduce side Join
  • Distributed Join
  • Distributed Cache
  • Counters
  • Joining Multiple datasets in MapReduce
Advance MapReduce - case B
  • MapReduce internals
  • Understanding Input Format
  • Custom Input Format
  • Using Writable and Comparable
  • Understanding Output Format
  • Sequence Files
  • JUnit and MRUnit Testing Frameworks
Apache Pig
  • PIG vs MapReduce
  • PIG Architecture & Data types
  • PIG Latin Relational Operators
  • PIG Latin Join and CoGroup
  • PIG Latin Group and Union
  • Describe, Explain, Illustrate
  • PIG Latin: File Loaders & UDF
Apache Hive and HiveQL
  • What is Hive
  • Hive DDL – Create/Show Database
  • Hive DDL – Create/Show/Drop Tables
  • Hive DML – Load Files & Insert Data
  • Hive SQL – Select, Filter, Join, Group By
  • Hive Architecture & Components
  • Difference between Hive and RDBMS
Advance HiveQL
  • Multi-Table Inserts
  • Joins
  • Grouping Sets, Cubes, Rollups
  • Custom Map and Reduce scripts
  • Hive SerDe
  • Hive UDF
  • Hive UDAF
Apache Flume, Sqoop, Oozie
  • Sqoop – How Sqoop works
  • Sqoop Architecture
  • Flume – How it works
  • Flume Complex Flow – Multiplexing
  • Oozie – Simple/Complex Flow
  • Oozie Service/ Scheduler
  • Use Cases – Time and Data triggers
NoSQL Databases
  • CAP theorem
  • RDBMS vs NoSQL
  • Key Value stores: Memcached, Riak
  • Key Value stores: Redis, Dynamo DB
  • Column Family: Cassandra, HBase
  • Graph Store: Neo4J
  • Document Store: MongoDB, CouchDB
Apache HBase
  • When/Why to use HBase
  • HBase Architecture/Storage
  • HBase Data Model
  • HBase Families/ Column Families
  • HBase Master
  • HBase vs RDBMS
  • Access HBase Data
Apache Zookeeper
  • What is Zookeeper
  • Zookeeper Data Model
  • ZNokde Types
  • Sequential ZNodes
  • Installing and Configuring
  • Running Zookeeper
  • Zookeeper use cases
Hadoop 2.0, YARN, MRv2
  • Hadoop 1.0 Limitations
  • MapReduce Limitations
  • HDFS 2: Architecture
  • HDFS 2: High availability
  • HDFS 2: Federation
  • YARN Architecture
  • Classic vs YARN
  • YARN multitenancy
  • YARN Capacity Scheduler

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Why Chooce Us

  • Training from professional Big Data Hadoop expert
  • 10 years of experience
  • Training and Internship combined
  • Real-Time Development experience
  • Fully Equapaid Lab, With AC & WIFI Internet available
  • Support and Careers Advice
  • We Offer Quality Training
  • and so much more…
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