Big Data Hadoop Certification Training
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.
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 Certs Learning’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
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.
- 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
- 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
- Hadoop Market is expected to reach $99.31B by 2022 at a CAGR of 42.1% -Forbes
- McKinsey predicts that by 2018 there will be a shortage of 1.5M data experts
- Average Salary of Big Data Hadoop Developers is $97k
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, Certs Learning offers a complimentary self-paced course on “Java essentials for Hadoop” when you enroll for the Big Data and Hadoop Course.
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
Get detailed course syllabus in
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
A. Find out the frequency of books published each year. (Hint: Sample dataset will be provided)
B. Find out in which year maximum number of books were published
C. 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
A. Find list of Airports operating in the Country India
B. Find the list of Airlines having zero stops
C. List of Airlines operating with code share
D. Which country (or) territory having highest Airports
E. 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