Big Data Hadoop training will make you expert in HDFS, Yarn, MapReduce, Pig, Hive, HBase, Oozie, Flume and Sqoop using real-time use cases on Retail, Social Media, Aviation, Tourism, Finance domain.
What are the pre-requisites for the Hadoop Course?
As such, there are no pre-requisites for learning Hadoop. Knowledge of Core Java and SQL will be beneficial, but certainly not a mandate. If you wish to brush-up Core-Java skills, Edureka offer you a complimentary self-paced course, i.e. "Java essentials for Hadoop" when you enroll in Big Data Hadoop Certification course.
For practicals, we will help you to setup Edureka's Virtual Machine in your System with local access. The detailed installation guide will be present in LMS for setting up the environment. In case, your system doesn't meet the pre-requisites e.g. 4GB RAM, you will be provided remote access to the Edureka cluster for doing practical. For any doubt, the 24*7 support team will promptly assist you. Edureka Virtual Machine can be installed on Mac or Windows machine and the VM access will continue even after the course is over, so that you can practice.
1. Fetch the data into a Hadoop Distributed File System and analyze it with the help of MapReduce, Pig and Hive to find the top rated links based on the user comments, likes etc.
2. Using MapReduce, convert the semi-structured format (XML data) into a structured format and categorize the user rating as positive and negative for each of the thousand links.
3. Push the output HDFS and then feed it into PIG, which splits the data into two parts: Category data and Ratings data.
4. Write a fancy Hive Query to analyze the data further and push the output is into relational database (RDBMS) using Sqoop.
5. Use a web server running on grails/java/ruby/python that renders the result in real time processing on a website.
3. Top 20 high air-revenue destinations, i.e the 20 cities that generate high airline revenues for travel, so that the discount offers can be given to attract more bookings for these destinations.
Problem Statement: Find the number of cases per location and categorize the count with respect to reason for taking loan and display the average risk score.
Professionals from around the globe have benefited from Edureka's Big Data Hadoop Certification course. Some of the top places that our learners come from include San Francisco, Bay Area, New York, New Jersey, Houston, Seattle, Toronto, London, Berlin, UAE, Singapore, Australia, New Zealand, Bangalore, New Delhi, Mumbai, Pune, Kolkata, Hyderabad and Gurgaon among many.
Edureka’s Big Data Hadoop online training is one of the most sought after in the industry and has helped thousands of Big Data professionals around the globe bag top jobs in the industry. This online training includes lifetime access, 24X7 support for your questions, class recordings and mobile access. Our Big Data Hadoop certification also include an overview of Apache Spark for distributed data processing.
Once you are successfully through the project (Reviewed by a edureka expert), you will be awarded with edureka’s Big Data and Hadoop certificate.
Learning Objectives - In this module, you will understand Big Data, the limitations of the existing solutions for Big Data problem, how Hadoop solves the Big Data problem, the common Hadoop ecosystem components, Hadoop Architecture, HDFS, Anatomy of File Write and Read, how MapReduce Framework works.
Learning Objectives - In this module, you will learn the Hadoop Cluster Architecture, Important Configuration files in a Hadoop Cluster, Data Loading Techniques, how to setup single node and multi node hadoop cluster.
Topics - Hadoop 2.x Cluster Architecture - Federation and High Availability, A Typical Production Hadoop Cluster, Hadoop Cluster Modes, Common Hadoop Shell Commands, Hadoop 2.x Configuration Files, Single node cluster and Multi node cluster set up Hadoop Administration.
Learning Objectives - In this module, you will understand Hadoop MapReduce framework and the working of MapReduce on data stored in HDFS. You will understand concepts like Input Splits in MapReduce, Combiner & Partitioner and Demos on MapReduce using different data sets.
Topics - MapReduce Use Cases, Traditional way Vs MapReduce way, Why MapReduce, Hadoop 2.x MapReduce Architecture, Hadoop 2.x MapReduce Components, YARN MR Application Execution Flow, YARN Workflow, Anatomy of MapReduce Program, Demo on MapReduce. Input Splits, Relation between Input Splits and HDFS Blocks, MapReduce: Combiner & Partitioner, Demo on de-identifying Health Care Data set, Demo on Weather Data set.
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.
Topics - Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format, Xml file Parsing using MapReduce.
Learning Objectives - In this module, you will learn Pig, types of use case we can use Pig, tight coupling between Pig and MapReduce, and Pig Latin scripting, PIG running modes, PIG UDF, Pig Streaming, Testing PIG Scripts. Demo on healthcare dataset.
Topics - About Pig, MapReduce Vs Pig, Pig Use Cases, Programming Structure in Pig, Pig Running Modes, Pig components, Pig Execution, Pig Latin Program, Data Models in Pig, Pig Data Types, Shell and Utility Commands, Pig Latin : Relational Operators, File Loaders, Group Operator, COGROUP Operator, Joins and COGROUP, Union, Diagnostic Operators, Specialized joins in Pig, Built In Functions ( Eval Function, Load and Store Functions, Math function, String Function, Date Function, Pig UDF, Piggybank, Parameter Substitution ( PIG macros and Pig Parameter substitution ), Pig Streaming, Testing Pig scripts with Punit, Aviation use case in PIG, Pig Demo on Healthcare Data set.
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.
Topics - Hive Background, Hive Use Case, About Hive, Hive Vs Pig, Hive Architecture and Components, Metastore in Hive, Limitations of Hive, Comparison with Traditional Database, Hive Data Types and Data Models, Partitions and Buckets, 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 Data set.
Learning Objectives - In this module, you will understand Advanced Hive concepts such as UDF, Dynamic Partitioning, Hive indexes and views, optimizations in hive. You will also acquire in-depth knowledge of HBase, HBase Architecture, running modes and its components.
Topics - Hive QL: Joining Tables, Dynamic Partitioning, Custom Map/Reduce Scripts, Hive Indexes and views Hive query optimizers, Hive : Thrift Server, User Defined Functions, HBase: Introduction to NoSQL Databases and HBase, HBase v/s RDBMS, HBase Components, HBase Architecture, Run Modes & Configuration, HBase Cluster Deployment.
Learning Objectives - This module will cover Advanced HBase concepts. We will see demos on Bulk Loading , Filters. You will also learn what Zookeeper is all about, how it helps in monitoring a cluster, why HBase uses Zookeeper.
Learning Objectives - In this module you will learn Spark ecosystem and its components, how scala is used in Spark, SparkContext. You will learn how to work in RDD in Spark. Demo will be there on running application on Spark Cluster, Comparing performance of MapReduce and Spark.
Topics - What is Apache Spark, Spark Ecosystem, Spark Components, History of Spark and Spark Versions/Releases, Spark a Polyglot, What is Scala?, Why Scala?, SparkContext, RDD.
Learning Objectives - In this module, you will understand working of multiple Hadoop ecosystem components together in a Hadoop implementation to solve Big Data problems. We will discuss multiple data sets and specifications of the project. This module will also cover Flume & Sqoop demo, Apache Oozie Workflow Scheduler for Hadoop Jobs, and Hadoop Talend integration.
Topics - Flume and Sqoop Demo, Oozie, Oozie Components, Oozie Workflow, Scheduling with Oozie, Demo on Oozie Workflow, Oozie Co-ordinator, Oozie Commands, Oozie Web Console, Oozie for MapReduce, PIG, Hive, and Sqoop, Combine flow of MR, PIG, Hive in Oozie, Hadoop Project Demo, Hadoop Integration with Talend.
There will be 30hrs of Online Live Instructor-led Classes. Depending on the batch you select, it can be: i) 10 live classes of 3 hrs each over Weekend or, ii) 15 live classes of 2 hrs each on Weekdays.
There are hands-on exercises associated with every module in the course. We anticipate that you will spend minimum 40 hours working on assignments to ensure better assimilation of concepts. Edureka will provide the required setup for doing practicals.
You get lifetime access to the Learning Management System (LMS). You will be able to access class recordings on LMS and lifetime feature shall also benefit you to get the future upgraded versions without paying any extra fee.
We have round the clock support available to help you with any technical queries. All the queries are tracked as tickets and you get a guaranteed response from a support engineer. If required, the support team can also provide you Live support by accessing your machine remotely. Please be assured!
Towards the end of the course, you will work on a project. Edureka certifies you in Big Data and Hadoop course based on the project reviewed by our expert panel. Anyone certified by edureka will be able to demonstrate practical expertise in Big Data and Hadoop.
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