big data & hadoop applications in environment

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Applications of BIG Data in Environment


Introduction to Big Data

Big data is the term for 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

Systems / Enterprises generate huge amount of data from Terabytes to Petabytes

Understanding Big Data can give you innovative solutions to various queries.


Applications of BIG Data in Environment

• Weather Forecasting• Biodiversity Monitoring• Monitoring Tectonic Movements• Global Warming


BIG Data Applications in Environment

Biodiversity Monitoring

Till recent times biodiversity monitoring used to be primitive. It was either done via human action or mapping via planes & satellites. The primary drawback here was that no direct insight into bio-diversity composition & health was provided. BIG Data analytics via sensors placed in bio-diverse regions track parameters relating to soil health, afforestation/deforestation, species growth and any external conflicts. This provides comprehensive bio-diversity monitoring.

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Weather Forecasting

Big Data is enabling the provision of real-time weather forecasting via collecting data from sensors across geographies. Earlier, weather forecasting was moderately accurate and couldn’t predict weather beyond the next 4-5 days. That is now changing. IBM's Deep Thunder Project, powered by powerful mathematical models, is utilizing sensor-based data to provide advanced forecasts with a minimum accuracy of 90%.

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Monitoring Tectonic Movements

BIG Data derived from underwater sensors can accurately monitor the movement of tectonic plates. Not only does this provide data regarding the environment constitution of earth, it also can predict the on-set of earthquakes, floods and tsunamis, which can result in the saving of human lives.

These sensors are also used to discover & track rare under-water species.

BIG Data Applications in Environment


Global Warming

Deforestation is a primary cause for Global Warming. What is a scalable solution to keep that in check? Or, how do we keep track of rising sea levels & melting polar ice-caps?

BIG Data analytics provides vital-insights into the health & erosion of environmentally sensitive zones as well as helps ascertain protective measures to ensure that the effects of global warming can be reversed.



Current Examples of Big Data Application

Private Sector – NGO Partnership: HP Earth Insights and CI’s TEAM Network CI’s Tropical Ecology Assessment Monitoring (TEAM) Network serves as an “early warning system for nature”, with

a specific focus on biodiversity monitoring in tropical forests. They operate across 16 tropical forests across Asia, America & Latin America wherein they mine data relating to environmental health & trends of that particular zone. They also devise strategies for afforestation & minimization of man-animal conflict.



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How Are Insights Derived from BIG Data?

Say Hello to Hadoop Development :)




Introduction to Hadoop

Apache Hadoop is a framework that allows the distributed processing of large data sets across clusters of commodity computers using a simple programming model.

It is an Open-Source Data Management technology with extensive storage and distributed processing.

Hadoop CharacteristicsFlexible

Reliable

Economical

Scalable Get Started with BIG Data & Hadoop



Why Should You Learn Hadoop?

• Hadoop is a modern BIG Data processing framework which utilizes distributed computing clusters.

• It can process, manage and store unstructured data with absolute ease.

• Due to its scalability & effectiveness, companies are heavily adopting Hadoop.

• It has become the standard for storing, processing and analyzing big data.

• BIG Data & Hadoop professionals are in extremely high demand.

• This is the first step to becoming a data scientist or data architect.




Reports – 2015

Here are some of the other Reports that convey the same message:

• “It is expected that Hadoop and Big Data Analytics will grow to around $13.9 billion from 2012 to 2017.” (Markets and Markets Research Report)

• “The Data market currently with the fastest growth are Hadoop and NoSQL software and services.” (Technology Research Organization, Wikibon)

• “The Big Data market will likely hit $23.8 billion with a 31.7% rise per year.” (IDC Report)

• “Almost 90% organizations have embarked on Hadoop related projects and thus Hadoop skills are in huge demand.” (According to the Big Data Executive Survey 2013)

• “Companies hiring Hadoop professionals Analytics professionals obtain a 250 percent hike in their salaries.” (According to Analytics Industry Report)

Gartner had predicted, “Hadoop will be in most advanced analytics products by 2015.” By far and large, this prediction has turned out to be true. Hadoop is “The Technology” to harness Big Data




Predictions for 2015

According to the Forrester Report:

• A new data economy will arise, Hadooponomics. Due to its vast data prowess, enterprise adoption of Hadoop will become compulsory. All those companies, which were unsure of adopting Hadoop till 2014, are bound to adopt it in 2015

• The surge in demand for Hadoop job professionals will be fulfilled by Java professionals & other software engineers who will upgrade their skills

• The creation of SQL-on-Hadoop options will further enhance usability & adoption

• Due to the introduction of Yarn (Version 2.0), the usage of Hadoop will go beyond analytics. It will transform into an application platform giving birth to new frameworks in the Hadoop ecosystem

• New Hadoop sources will emerge from organizations like Oracle, HP, Tibco and SAP. Vendors such as Red Hat, VMware and Microsoft will include Hadoop in their operating Systems




Why SkillSpeed?

Course Curriculum

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Course Topics

Module 1

Introduction to Big Data and Hadoop

Module 2

HDFS Internals, Hadoop

Configurations and Data Loading

Module 3

Introduction to Map Reduce

Module 4

Advanced Map Reduce Concepts

Module 5

Introduction to Pig

Module 6

Advanced Pig and Introduction to Hive

Module 7

Advanced Hive Concepts

Module 8

Extending Hive and HBase Introduction

Module 9

Advanced HBase and Oozie Introduction

Module 10

Project Set-up Discussion




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