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Big Data: How it works

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Christian Hissibini

I am a Tech enthusiast who loves to blend Dev & Design on Web and Mobile Platforms. I am also a Windows Platform Dev MVP


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Big Data: How it works

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Before discovering how big data can work for your business, you should first understand where it comes from. The sources for big data generally fall into one of three categories:

Streaming data

This category includes data that reaches your IT systems from a web of connected devices. You can analyze this data as it arrives and make decisions on what data to keep, what not to keep and what requires further analysis.

Social media data

The data on social interactions is an increasingly attractive set of information, particularly for marketing, sales and support functions. It’s often in unstructured or semistructured forms, so it poses a unique challenge when it comes to consumption and analysis.

Publicly available sources 

Massive amounts of data are available through open data sources like the US government’s data.gov, the CIA World Factbook or the European Union Open Data Portal.

 

 

After identifying all the potential sources for data, consider the decisions you’ll need to make once you begin harnessing information. These include:

How to store and manage it

Whereas storage would have been a problem several years ago, there are now low-cost options for storing data if that’s the best strategy for your business.

How much of it to analyze 

Some organizations don’t exclude any data from their analyses, which is possible with today’s high-performance technologies such as grid computing or in-memory analytics. Another approach is to determine upfront which data is relevant before analyzing it.

How to use any insights you uncover

The more knowledge you have, the more confident you’ll be in making business decisions. It’s smart to have a strategy in place once you

 

 

The final step in making big data work for your business is to research the technologies that help you make the most of big data and big data analytics. Consider:

  • Cheap, abundant storage.
  • Faster processors.
  • Affordable open source, distributed big data platforms, such as Hadoop.
  • Parallel processing, clustering, MPP, virtualization, large grid environments, high connectivity and high throughputs.
  • Cloud computing and other flexible resource allocation arrangements.

 

Ref
https://msdn.microsoft.com/ – https://blogs.nvidia.com –  https://www.sas.com/en_us/insights/analytics/machine-learning.html

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Christian Hissibini

I am a Tech enthusiast who loves to blend Dev & Design on Web and Mobile Platforms. I am also a Windows Platform Dev MVP

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