BIG Data & Hadoop Applications in Manufacturing

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Applications of BIG Data in Manufacturing


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.



• Accurate Demand Estimates• Reliable Supply Chain• Increased Asset Life• Go-To Market Plans


Reliable Supply Chain

It is crucial for industrial products to have apt storage & transport conditions to prevent any mishap. They have to be stored at particular temperatures, require specific lighting and apt packaging material. How do you ensure this on a real-time basis?

Sensors are installed in each step of the supply chain which provide real-time feedback on supply movements & conditions. Even if the required temperature goes down by 1/10th of a degree, the system immediately notifies the authorities. This almost completely negates the possibility of a mishap.

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Accurate Demand Estimates

Traditional demand generation models which look at only purchase history are going to become obsolete. Big Data solutions are capable of monitoring fluid market conditions and combining them with real-time customer demand forecasts.

Due to this, a manufacturing unit can accurately predict the demand for its product. Based on this it can optimize its operational & cost structures.

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Increased Asset Life

Equipment health is crucial for any manufacturing concern. Businesses today leverage equipment-condition data and predictive analytics to optimize maintenance cycles and improve operation efficiency.

BIG Data solutions monitor data including maintenance history, inventory cycles, demand cycles, labor expertise & safety to provide an unparalleled asset management experience.



Go-To Market Plans

Market pricing and planning can help companies maximize profits. For example, an agricultural company can use Hadoop to collectively analyse soil quality, weather predictions, crop demand and other factors to decide the best crop-mix for this season. This coupled with the supply-chain can result in the creation of an optimum Go-To market strategy.



Intel Intel harnesses Big Data to ensure the quality of each of its processors. Earlier, a chip has to undergo 19,000 QA

tests, however, with the integration of predictive analytics Intel optimized the QA process which significantly reduced the no. of required tests. Due to this, they incurred a meagre saving of $3 million in manufacturing costs for a single line of Intel Core processors.

Merck & Co This New Jersey - based drugmaker incorported BIG Data analytics into its vaccine manufacturing process. They

got insights ranging to supply chain optimization & equipment maintainence to go-to market stratgies & demand predictions. This enabled them to make smarter decisions, thus, resulting in higher profitability.


Examples of BIG Data in Manufacturing


© 2015 BlueCamphor Technologies (P) Ltd.

How Are Insights Derived from BIG Data?

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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

from Industry Experts

Instructor Led Live Virtual Sessions

Lifetime access to Course

Content via LMS

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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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