Big Data Hadoop Certification Training
Master Big Data and Hadoop to unlock great career opportunities as a Hadoop developer. Become a Hadoop expert by learning concepts like MapReduce, Yarn, Pig, Hive, HBase, Oozie, Flume and Sqoop. Get industry-ready with some of the best Big Data projects and real-life use-cases.
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 online Hadoop Training, you will be working on real-life industry use cases in Retail, Social Media, Aviation, Tourism and Finance domain using Edureka's Cloud Lab. 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. ... Read More »
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 online Hadoop Training, you will be working on real-life industry use cases in Retail, Social Media, Aviation, Tourism and Finance domain using Edureka’s Cloud Lab.
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, Spark, HBase, Hive, Pig, Oozie, Sqoop & Flume.
Edureka 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.
Big Data is one of the accelerating and most promising fields, considering all the technologies available in the IT market today. In order to take benefit of these opportunities, you need a structured training with the latest curriculum as per current industry requirements and best practices.
Besides strong theoretical understanding, you need to work on various real world big data projects using different Big Data and Hadoop tools as a part of solution strategy.
Additionally, you need the guidance of a Hadoop expert who is currently working in the industry on real world Big Data projects and troubleshooting day to day challenges while implementing them.
Big Data Hadoop Certification Training will help you to become a Big Data expert. It will hone your skills by offering you comprehensive knowledge on Hadoop framework, and the required hands-on experience for solving real-time industry-based Big Data projects. During Big Data & Hadoop course you will be trained by our expert instructors to:
- Master the concepts of HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator), & understand how to work with Hadoop storage & resource management.
- Understand MapReduce Framework
- Implement complex business solution using MapReduce
- Learn data ingestion techniques using Sqoop and Flume
- Perform ETL operations & data analytics using Pig and Hive
- Implementing Partitioning, Bucketing and Indexing in Hive
- Understand HBase, i.e a NoSQL Database in Hadoop, HBase Architecture & Mechanisms
- Integrate HBase with Hive
- Schedule jobs using Oozie
- Implement best practices for Hadoop development
- Understand Apache Spark and its Ecosystem
- Learn how to work with RDD in Apache Spark
- Work on real world Big Data Analytics Project
- Work on a real-time Hadoop cluster
Industry: Stock Market
Problem Statement: TickStocks, a small stock trading organization, wants to build a Stock Performance System. You have been tasked to create a solution to predict good and bad stocks based on their history. You also have to build a customized product to handle complex queries such as calculating the covariance between the stocks for each month.
Problem statement: MobiHeal is a mobile health organization that captures patient’s physical activities, by attaching various sensors on different body parts. These sensors measure the motion of diverse body parts like acceleration, the rate of turn, magnetic field orientation, etc. You have to build a system for effectively deriving information about the motion of different body parts like chest, ankle, etc.
Industry: Social Media
Problem Statement: Socio-Impact is a social media marketing company which wants to expand its business. They want to find the websites which have a low rank web page. You have been tasked to find the low-rated links based on the user comments, likes etc.
Problem Statement: A retail company wants to enhance their customer experience by analysing the customer reviews for different products. So that, they can inform the corresponding vendors and manufacturers about the product defects and shortcomings. You have been tasked to analyse the complaints filed under each product & the total number of complaints filed based on the geography, type of product, etc. You also have to figure out the complaints which have no timely response.
Problem Statement: A new company in the travel domain wants to start their business efficiently, i.e. high profit for low TCO. They want to analyse & find the most frequent & popular tourism destinations for their business. You have been tasked to analyse top tourism destinations that people frequently travel & top locations from where most of the tourism trips start. They also want you to analyze & find the destinations with costly tourism packages.
Problem Statement: A new airline company wants to start their business efficiently. They are trying to figure out the possible market and their competitors. You have been tasked to analyse & find the most active airports with maximum number of flyers. You also have to analyse the most popular sources & destinations, with the airline companies operating between them.
Industry: Banking and Finance
Problem Statement: A finance company wants to evaluate their users, on the basis of loans they have taken. They have hired you to find the number of cases per location and categorize the count with respect to the reason for taking a loan. Next, they have also tasked you to display their average risk score.
Industry: Media & Entertainment
Problem Statement: A new company in Media and Entertainment domain wants to outsource movie ratings & reviews. They want to know the frequent users who is giving review and rating consistently for most of the movies. You have to analyze different users, based on which user has rated the most number of movies, their occupations & their age-group.
Each class will be followed by practical assignments which can be completed before the next class.
- 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 Edureka’s CloudLab
- 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
Type of Certification
Format of Certification
Method of Obtaining Certification
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.
- 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
Learning Objectives: In this module, you will learn Hadoop Cluster Architecture, important configuration files of Hadoop Cluster, Data Loading Techniques using Sqoop & Flume, and how to setup Single Node and Multi-Node Hadoop Cluster.
- 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
Learning Objectives: In this module, you will understand Hadoop MapReduce framework comprehensively, the working of MapReduce on data stored in HDFS. You will also learn the advanced MapReduce concepts like Input Splits, Combiner & Partitioner.
- Traditional way vs MapReduce way
- Why MapReduce
- YARN Components
- YARN Architecture
- YARN MapReduce Application Execution Flow
- YARN Workflow
- Anatomy of MapReduce Program
- Input Splits, Relation between Input Splits and HDFS Blocks
- MapReduce: Combiner & Partitioner
- Demo of Health Care Dataset
- Demo of Weather Dataset
Learning Objectives: In this module, you will learn Advanced MapReduce concepts such as Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format and XML parsing.
- Distributed Cache
- Reduce Join
- Custom Input Format
- Sequence Input Format
- XML file Parsing using MapReduce
Learning Objectives: In this module, you will learn Apache Pig, types of use cases where we can use Pig, tight coupling between Pig and MapReduce, and Pig Latin scripting, Pig running modes, Pig UDF, Pig Streaming & Testing Pig Scripts. You will also be working on healthcare dataset.
- 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
Learning Objectives: This module will help you in understanding Hive concepts, Hive Data types, loading and querying data in Hive, running hive scripts and Hive UDF.
- Introduction to Apache Hive
- Hive vs Pig
- Hive Architecture and Components
- Hive Metastore
- Limitations of Hive
- Comparison with Traditional Database
- Hive Data Types and Data Models
- Hive Partition
- Hive Bucketing
- Hive Tables (Managed Tables and External Tables)
- Importing Data
- Querying Data & Managing Outputs
- Hive Script & Hive UDF
- Retail use case in Hive
- Hive Demo on Healthcare Dataset
Learning Objectives: In this module, you will understand advanced Apache Hive concepts such as UDF, Dynamic Partitioning, Hive indexes and views, and optimizations in Hive. You will also acquire in-depth knowledge of Apache HBase, HBase Architecture, HBase running modes and its components.
- Hive QL: Joining Tables, Dynamic Partitioning
- Custom MapReduce Scripts
- Hive Indexes and views
- Hive Query Optimizers
- Hive Thrift Server
- Hive UDF
- Apache HBase: Introduction to NoSQL Databases and HBase
- HBase v/s RDBMS
- HBase Components
- HBase Architecture
- HBase Run Modes
- HBase Configuration
- HBase Cluster Deployment
Learning Objectives: This module will cover advance Apache HBase concepts. We will see demos on HBase Bulk Loading & HBase Filters. You will also learn what Zookeeper is all about, how it helps in monitoring a cluster & why HBase uses Zookeeper.
- 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
Learning Objectives: In this module, you will learn what is Apache Spark, SparkContext & Spark Ecosystem. You will learn how to work in Resilient Distributed Datasets (RDD) in Apache Spark. You will be running application on Spark Cluster & comparing the performance of MapReduce and Spark.
- What is Spark
- Spark Ecosystem
- Spark Components
- What is Scala
- Why Scala
- Spark RDD
Learning Objectives: In this module, you will understand how multiple Hadoop ecosystem components work together to solve Big Data problems. This module will also cover Flume & Sqoop demo, Apache Oozie Workflow Scheduler for Hadoop Jobs, and Hadoop Talend integration.
- 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
1) Analyses of a Online Book Store:
- Find out the frequency of books published each year. (Hint: Sample dataset will be provided)
- Find out in which year maximum number of books were published
- Find out how many books were published based on ranking in the year 2002
Sample Dataset Description:
- The Book-Crossing dataset consists of 3 tables that will be provided to you
2) Airlines Analysis :
- Find list of Airports operating in the Country India
- Find the list of Airlines having zero stops
- List of Airlines operating with code share
- Which country (or) territory having highest Airports
- Find the list of Active Airlines in United state
Sample Dataset Description:
- In this use case, there are 3 data sets. Final_airlines, routes.dat, airports_mod.dat
You don’t have to worry about the system requirements as you will be executing your practicals on a Cloud LAB environment. This environment already contains all the necessary software that will be required to execute your practicals.