Project Description

Bigdata Apache Hadoop Certification Training

+91 9606046725

Big Data Hadoop Training Course is curated by Hadoop industry experts, and it covers in-depth knowledge on Big Data and Hadoop Ecosystem tools such as HDFS, YARN, MapReduce, Hive, Pig, HBase, Spark, Oozie, Flume and Sqoop. Throughout this Hadoop Training, you will be working on real-life industry use cases in Retail, Social Media, Aviation, Tourism, and Finance domain

Why should you take Bigdata Apache Hadoop Certification Training

Average Salary of Big Data Hadoop Developers is $135,000 (smecjobs.com salary data)

IT Industry in India and US predicts that by 2018 there will be a shortage of 1,500,000 data experts

The Hadoop Big Data analytics market is projected to grow to USD 40.69 Billion by 2021

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Curriculum
Course Details
Certification
Features

Bigdata Apache Hadoop Certification Training Course Curriculum

  • Linux (Ubuntu/Centos) – Tips and Tricks
  • Basic(core) Java Programming Concepts – OOPS

Learning Objectives: In this module, you will understand what Big Data is, the limitations of the traditional solutions for Big Data problems, how Hadoop solves those Big Data problems, Hadoop Ecosystem, Hadoop Architecture, HDFS, Anatomy of File Read and Write & how MapReduce works.

Topics:

  • Introduction to Big Data & Big Data Challenges
  • Limitations & Solutions of Big Data Architecture
  • Hadoop & its Features
  • Hadoop Ecosystem
  • Hadoop 2.x Core Components
  • Hadoop Storage: HDFS (Hadoop Distributed File System)
  • Hadoop Processing: MapReduce Framework
  • Different Hadoop Distributions
  • Hadoop 2.x Architecture
  • Typical workflow
  • HDFS Commands
  • Writing files to HDFS
  • Reading files from HDFS
  • Rack awareness
  • Hadoop daemons
  • Before MapReduce
  • MapReduce overview
  • Word count problem
  • Word count flow and solution
  • MapReduce flow
  • Data Types
  • File Formats
  • Explain the Driver, Mapper and Reducer code
  • Configuring development environment – Eclipse
  • Writing unit test
  • Running locally
  • Running on cluster
  • Hands on exercises
  • Anatomy of MapReduce job run
  • Job submission
  • Job initialization
  • Task assignment
  • Job completion
  • Job scheduling
  • Job failures
  • Shuffle and sort
  • Hands on exercises
  • File Formats – Sequence Files
  • Compression Techniques
  • Input Formats – Input splits & records, text input, binary input
  • Output Formats – text output, binary output, lazy output
  • Hands on exercises
  • Counters
  • Side data distribution
  • MapReduce combiner
  • MapReduce partitioner
  • MapReduce distributed cache
  • Hands exercises
  • Hive Architecture
  • Types of Metastore
  • Hive Data Types
  • HiveQL
  • File Formats – Parquet, ORC, Sequence and Avro Files Comparison
  • Partitioning & Bucketing
  • Hive JDBC Client
  • Hive UDFs
  • Hive Serdes
  • Hive on Tez
  • Hands-on exercises
  • Integration with Tableau
  • Introduction to Apache Pig
  • MapReduce vs Pig
  • Pig Components & Pig Execution
  • Pig Data Types & Data Models in Pig
  • Pig Latin Programs
  • Shell and Utility Commands
  • Pig UDF & Pig Streaming
  • Testing Pig scripts with Punit
  • Aviation use-case in PIG
  • Pig Demo of Healthcare Dataset
  • HBase Data Model
  • HBase Shell
  • HBase Client API
  • Hive Data Loading Techniques
  • Apache Zookeeper Introduction
  • ZooKeeper Data Model
  • Zookeeper Service
  • HBase Bulk Loading
  • Getting and Inserting Data
  • HBase Filters
  • Sqoop Architecture
  • Sqoop Import Command Arguments, Incremental Import
  • Sqoop Export
  • Sqoop Jobs
  • Hands-on exercises
  • Flume Architecture
  • Flume Agent Setup
  • Types of sources, channels, sinks Multi Agent Flow
  • Hands-on exercises
  • Spark Basics
  • What is Apache Spark?
  • Spark Installation
  • Spark Configuration
  • Spark Context
  • Using Spark Shell
  • Resilient Distributed Datasets (RDDs) – Features, Partitions, Tuning Parallelism
  • Functional Programming with Spark
  • ark Basics
  • What is Apache Spark?
  • Spark Installation
  • Spark Configuration
  • Spark Context
  • Using Spark Shell
  • Resilient Distributed Datasets (RDDs) – Features, Partitions, Tuning Parallelism
  • Functional Programming with Spark
  • Oozie
  • Oozie Components
  • Oozie Workflow
  • Scheduling Jobs with Oozie Scheduler
  • Demo of Oozie Workflow
  • Oozie Coordinator
  • Oozie Commands
  • Oozie Web Console
  • Oozie for MapReduce
  • Combining flow of MapReduce Jobs
  • Hive in Oozie
  • Hadoop Project Demo
  • Hadoop Talend Integration
  • Log File Analysis covering Flume, HDFS, MR/Pig, Hive, Tableau
  • Crime Data Analysis Covering Oozie, Sqoop, HDFS, Hive, HBase, RestFul Client.
  • Hadoop Use Cases in Insurance Domain
  • Hadoop Use Cases in Retail Domain

Like the Course Curriculum? Enroll Now or Get the free career path

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+91 7356455577
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Bigdata Apache Hadoop Certification Training Course Description

Hadoop is an Apache project (i.e. an open source software) to store & process Big Data. Hadoop stores Big Data in a distributed & fault tolerant manner over commodity hardware. Afterwards, Hadoop tools are used to perform parallel data processing over HDFS (Hadoop Distributed File System).

As organisations have realized the benefits of Big Data Analytics, so there is a huge demand for Big Data & Hadoop professionals. Companies are looking for Big data & Hadoop experts with the knowledge of Hadoop Ecosystem and best practices about HDFS, MapReduce, HBase, Hive, Pig, Oozie, Sqoop & Flume.

Hadoop Training is designed to make you a certified Big Data practitioner by providing you rich hands-on training on Hadoop Ecosystem. This Hadoop developer certification training is stepping stone to your Big Data journey and you will get the opportunity to work on various Big data projects.

  • In-depth knowledge of Big Data and Hadoop including HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator) & MapReduce
  • Comprehensive knowledge of various tools that fall in Hadoop Ecosystem like Pig, Hive, Sqoop, Flume, Oozie, and HBase
  • The capability to ingest data in HDFS using Sqoop & Flume, and analyze those large datasets stored in the HDFS
  • The exposure to many real-world industry-based projects which will be executed in SMEC Cloud Networks. Mini Datacenter in the Training Division
  • Projects which are diverse in nature covering various data sets from multiple domains such as banking, telecommunication, social media, insurance, and e-commerce
  • Rigorous involvement of a Hadoop expert throughout the Big Data Hadoop Training to learn industry standards and best practices
  • Software Developers, Project Managers
  • Software Architects
  • ETL and Data Warehousing Professionals
  • Data Engineers
  • Data Analysts & Business Intelligence Professionals
  • DBAs and DB professionals
  • Senior IT Professionals
  • Testing professionals
  • Mainframe professionals
  • Graduates looking to build a career in Big Data Field

There are no such prerequisites for Big Data & Hadoop Course. However, prior knowledge of Core Java and SQL will be helpful but is not mandatory. Further, to brush up your skills, SMEC Technology offers a Python and Java as an addon program.

Bigdata Apache Hadoop Certification Training  Training Features

Be future ready. Start learning –  Bigdata Apache Hadoop Certification Training

Talk to Bigdata Apache Hadoop Certification Training Expert?
+91 9606046725
+91 7356455577
Call Now +91 9606046725
Whatsapp Now
SMS Now