Hadoop - Big Data Development & Analytics - V2
!! Apache Community Delivers Big Data Solution to Advance Enterprise Analytics !!
The data revolution is upon us and Hadoop is THE leading Big Data platform. Fortune 500 companies are using it for storing and analyzing extremely large datasets, while other companies are realizing its potential and preparing their budgets for future Big Data positions. It's the elephant in Big Data's room!
Hey !! Can you Believe you can built your OWN Super Computer
First Class Room and Real Training Program of Big Data Hadoop version 2 in India
First Time in India - Built MapReduce Jobs in Python Language
DELIVERANCES BY LINUXWORLD :
- Project Certificate by LinuxWorld Informatics pvt. ltd. (only after successful completion of projects)
- Training Certificate by LinuxWorld - Training & Development Center
- DVD - Containing Software
- Extra Software and Tools, if Required
- Material in Soft and Hard Copy
- 24 x 7 Wi Fi Enabled Lab Facility
- Life Time Membership Card
- Expert faculty having 12 + yrs of industrial exposure
- Practical Implementation by having hands on experience on live demo and project
- Job Assistance
Towards the end of the Course, you will be working on a live project which will be a large dataset and you will be using PIG, HIVE, HBase and MapReduce to perform Big Data analytics. The final project is a real life business case on some open data set. There is not one but a large number of datasets which are a part of the Big Data and Hadoop Program.
Here are some of the data sets on which you may work as a part of the project work:
- Twitter Data Analysis : Twitter data analysis is used to understand the hottest trends by dwelling into the twitter data. Using flume data is fetched from twitter to Hadoop in JSON format. Using JSON-serde twitter data is read and fed into HIVE tables so that we can do different analysis using HIVE queries. For eg: Top 10 popular tweets etc.
- Stack Exchange Ranking and Percentile data-set : Stack Exchange is a place where you will find enormous data from multiple websites of Stack Group (like: stack overflow) which is open sourced. The place is a gold mine for people who wants to come up with several POCís and are searching for suitable data-sets. In there you may query out the data you are interested in which will contain more than 50,000 odd records. For eg: You can download StackOverflow Rank and Percentile data and find out the top 10 rankers.
- Loan Dataset : The project is designed to find the good and bad URL links based on the reviews given by the users. The primary data will be highly unstructured. Using MR jobs the data will be transformed into structured form and then pumped to HIVE tables. Using Hive queries we can query out the information very easily. In the phase two we will feed another dataset which contains the corresponding cached web pages of the URLís into HBASE. Finally the entire project is showcased into a UI where you can check the ranking of the URL and view the cached page.
- Data -sets by Government: These Data sets could be like Worker Population Ratio (per 1000) for persons of age (15-59) years according to the current weekly status approach for each state/UT.
- Machine Learning Dataset like Badges datasets : Such dataset is for system to encode names, for example +/- label followed by a personís name.
- NYC Data Set: NYC Data Set contains the day to day records of all the stocks. It will provide you with the information like opening rate, closing rate, etc for individual stocks. Hence, this data is highly valuable for people you have to make decision based on the market trends. One of the analysis which is very popular and can be done on this data set is to find out the Simple Moving Average which helps them to find the crossover action.
- Weather Dataset : It has all the details of weather over a period of time using which you may find out the highest, lowest or average temperature. In addition, you can choose your own dataset and create a project around that as well.
Why Learn Big Data and Hadoop?
- What is the Big Data problem?
- BiG Data! A Worldwide Problem?
- Apache Hadoop! A Solution for Big Data!
- Some of the top companies using Hadoop:
- Opportunities for Hadoop Administrator!
Big Data is a set of unstructured and structured data that is complex in nature and is growing exponentially with each passing day. Organizations are facing a major challenge in storing and utilizing this enormous data. This problem spans across the world because of a serious dearth of skilled programmers.
"The United States alone faces a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data."
According to Wikipedia, ďBig data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.Ē In simpler terms, Big Data is a term given to large volumes of data that organizations store and process. However, It is becoming very difficult for companies to store, retrieve and process the ever-increasing data. If any company gets hold on managing its data well, nothing can stop it from becoming the next BIG success!
The problem lies in the use of traditional systems to store enormous data. Though these systems were a success a few years ago, with increasing amount and complexity of data, these are soon becoming obsolete. The good news is - Hadoop, which is not less than a panacea for all those companies working with BIG DATA in a variety of applications has become an integral part for storing, handling, evaluating and retrieving hundreds or even petabytes of data.
Hadoop is an open source software framework that supports data-intensive distributed applications. Hadoop is licensed under the Apache v2 license. It is therefore generally known as Apache Hadoop. Hadoop has been developed, based on a paper originally written by Google on MapReduce system and applies concepts of functional programming. Hadoop is written in the Java programming language and is the highest-level Apache project being constructed and used by a global community of contributors. Hadoop was developed by Doug Cutting and Michael J. Cafarella. And just donít overlook the charming yellow elephant you see, which is basically named after Dougís sonís toy elephant!
The importance of Hadoop is evident from the fact that there are many global MNCs that are using Hadoop and consider it as an integral part of their functioning, such as companies like Yahoo and Facebook! On February 19, 2008, Yahoo! Inc. established the world's largest Hadoop production application. The Yahoo! Search Webmap is a Hadoop application that runs on over 10,000 core Linux cluster and generates data that is now widely used in every Yahoo! Web search query.
Facebook, a $5.1 billion company has over 1 billion active users in 2012, according to Wikipedia. Storing and managing data of such magnitude could have been a problem, even for a company like Facebook. But thanks to Apache Hadoop! Facebook uses Hadoop to keep track of each and every profile it has on it, as well as all the data related to them like their images, posts, comments, videos, etc.
Opportunities for You are infinite - from a Hadoop Developer, to a Hadoop Tester or a Hadoop Architect, and so on. If cracking and managing BIG Data is your passion in life, then think no more and Join course and carve a niche for yourself!
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Apache - BigData Hadoop Training in Jaipur
This series will get you up to speed on Big Data and Hadoop. Topics include how to install, configure and manage a single and multi-node Hadoop cluster, configure and manage HDFS, write MapReduce jobs and work with many of the projects around Hadoop such as Pig, Hive, HBase, Sqoop, and Zookeeper. Topics also include configuring Hadoop in the cloud and troubleshooting a multi-node Hadoop cluster.
The Apache Hadoop software is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Hadoop is a software framework for storing and processing Big Data. It is an open-source tool build on java platform and focuses on improved performance in terms of data processing on clusters of commodity hardware.
- Hadoop comprises of multiple concepts and modules like HDFS, Map-Reduce, HBASE, PIG, HIVE, SQOOP and ZOOKEEPER to perform the easy and fast processing of huge data
- Hadoop conceptually different from Relational databases and can process the high volume, high velocity and high variety of data to generate value
- Master the concepts of Hadoop Distributed File System and MapReduce framework
- Setup a Hadoop Cluster
- Understand Data Loading Techniques using Sqoop and Flume
- Program in MapReduce (Both MRv1 and MRv2)
- Learn to write Complex MapReduce programs
- Program in YARN (MRv2)
- Perform Data Analytics using Pig and Hive
- Implement HBase, MapReduce Integration, Advanced Usage and Advanced Indexing
- Have a good understanding of ZooKeeper service
- New features in Hadoop 2.0 -- YARN, HDFS Federation, NameNode High Availability
- Implement best Practices for Hadoop Development and Debugging
- Implement a Hadoop Project
- Work on a Real Life Project on Big Data Analytics and gain Hands on Project Experience
After the completion of the Big Data and Hadoop Course, you should be able to:
Hiring IT professionals who are certified as Hadoop BigData allows many organizations to increase their ratio of servers to administrators, enabling them to be more cost effective in building out their infrastructures without needing to bring on additional resources.
- Hadoop - Why Hadoop, Scaling, Distributed Framework, Hadoop v/s RDBMS, Brief history of Hadoop, Problems with traditional large-scale systems, Requirements for a new approach, Anatomy of a Hadoop cluster, Other Hadoop Ecosystem components
- Hadoop Architecture - What is Big Data, Hadoop Architecture, Hadoop ecosystem components, Hadoop Storage: HDFS, Hadoop Processing: MapReduce Framework, Hadoop Server Roles: NameNode, Secondary NameNode, and DataNode, Anatomy of File Write and Read.
- Hadoop Cluster Configuration and Data Loading - Pseudo mode, Cluster mode, Installation of Java, Hadoop, Configurations of Hadoop, Hadoop Processes ( NN, SNN, JT, DN, TT), Temporary directory, UI, Common errors when running Hadoop cluster, Solutions, Hadoop Cluster Architecture, Hadoop Cluster Configuration files, Hadoop Cluster Modes, Multi-Node Hadoop Cluster, A Typical Production Hadoop Cluster, MapReduce Job execution, Common Hadoop Shell commands, Data Loading Techniques: FLUME, SQOOP, Hadoop Copy Commands, Hadoop Project: Data Loading.
- Hadoop MapReduce framework - Hadoop Data Types, Hadoop MapReduce paradigm, Map and Reduce tasks, MapReduce Execution Framework, Partitioners and Combiners, Input Formats (Input Splits and Records, Text Input, Binary Input, Multiple Inputs), Output Formats (TextOutput, BinaryOutPut, Multiple Output), Hadoop Project: MapReduce Programming.
- Advance MapReduce - Counters, Custom Writables, Unit Testing: JUnit and MRUnit testing framework, Error Handling, Tuning, Advance MapReduce, Hadoop Project: Advance MapReduce programming and error handling.
- Pig and Pig Latin - Installing and Running Pig, Grunt, Pig's Data Model, Pig Latin, Developing & Testing Pig Latin Scripts, Writing Evaluation, Filter, Load & Store Functions, Hadoop Project: Pig Scripting.
- Hive and HiveQL - Hive Architecture and Installation, Comparison with Traditional Database, HiveQL: Data Types, Operators and Functions, Hive Tables(Managed Tables and External Tables, Partitions and Buckets, Storage Formats, Importing Data, Altering Tables, Dropping Tables), Querying Data (Sorting And Aggregating, Map Reduce Scripts, Joins & Subqueries, Views, Map and Reduce side Joins to optimize Query).
- Advance Hive, NoSQL Databases and HBase - Hive: Data manipulation with Hive, User Defined Functions, Appending Data into existing Hive Table, Custom Map/Reduce in Hive, Hadoop Project: Hive Scripting, HBase: Introduction to HBase, Client API's and their features, Available Client, HBase Architecture, MapReduce Integration.
- Advance HBase and ZooKeeper - HBase: Advanced Usage, Schema Design, Advance Indexing, Coprocessors, Hadoop Project: HBase tables The ZooKeeper Service: Data Model, Operations, Implementation, Consistency, Sessions, States.
- Hadoop 2.0, MRv2 and YARN - Schedulers:Fair and Capacity, Hadoop 2.0 New Features: NameNode High Availability, HDFS Federation, MRv2, YARN, Running MRv1 in YARN, Upgrade your existing MRv1 code to MRv2, Programming in YARN framework.
- Hadoop Project Environment and Apache Oozie - In this module, you will understand how multiple Hadoop ecosystem components work 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 Apache Oozie Workflow Scheduler for Hadoop Jobs.
The Hadoop Training is designed for:
- wants to architect a project using Hadoop and its Eco System components
- wants to develop Map Reduce programs to handle enormous amounts of data
- a Business Analyst or Data Warehousing person looking at alternative approach to data analysis and storage
- Professionals aspiring to make career in Big Data Analytics using Hadoop Framework. Software Developers/Engineers, Business analysts, IT managers, Hadoop system administrators
- Administrators and storage administrators interested in, or responsible for, maintaining large storage clusters
- Learn to store, manage, retrieve and analyze Big Data on clusters of servers in the cloud using the Hadoop eco-system
- Become one of the most in-demand IT professional in the world today
- Don't just learn Hadoop development but also learn how to analyze large amounts of data to bring out insights
- Relevant examples and cases make the learning more effective and easier
- Gain hands-on knowledge through the problem solving based approach of the course along with working on a project at the end of the course
- Learn to analyze BigData business problems using Hadoop in a variety of fields like retail, FMCG, financial services, telecom, etc.
- Opportunity for you: Apache Hadoop is solution to Big data Problem that companies are facing today but there is a lack of skilled professionals
- There is a huge demand of Hadoop professionals
- Recognition in the industry
- Increased customer confidence
- Proof of knowledge and skills
Any experience of Linux environment will be helpful but not essential
Cloudera Certified Administrator for Apache Hadoop (CCAH)
Know the Methods Used by Top Administrators
Individuals who achieve Cloudera Certified Administrator for Apache Hadoop (CCAH) accreditation have demonstrated their technical knowledge, skill, and ability to configure, deploy, maintain, and secure an Apache Hadoop cluster.
These are the current exam sections and the percentage of the exam devoted to these topics.
- HDFS (38%)
- MapReduce (10%)
- Hadoop Cluster Planning (12%)
- Hadoop Cluster Installation and Administration (17%)
- Resource Management (6%)
- Monitoring and Logging (12%)
- Ecosystem (5%)
- Exam Code: CCA-410
- Number of Questions: 60 questions
- Time Limit: 90 minutes
- Passing Score: 70%
- Language: English, Japanese
- Price: USD $295, AUD $300, EUR Ä215, GBP £185, JPY •28,500
Certification program requirements:
- There are no restrictions on eligibility to take this exam.
- If Know a , that would give added advantages : A Cloudera Administrator Training for Apache Hadoop Configuring, Deploying, and Maintaining a Hadoop Cluster
- Real-world experience is the best preparation for this hands-on exam.
The following audiences may be interested in earning the Red Hat Certificate of Expertise in Storage Server:
- System administrators, architects, and others who need to demonstrate their skills, knowledge, and ability in using Hadoop Bigdata
- 5 days exam bootcamp is conducted keeping in mind the revision of entire course in rapid track and by imparting the tips and tricks to easily crack the paper.
- Mock tests are conducted to judge the student's knowledge and then help them in improving their respective weak areas to excel in the exam.