Big Data

Big data is a term that explains the big volume of data – both organized/structured and unstructured – that inundates a company over a day-to-day basis. But it isn’t the quantity of data that’s important. It’s what organizations/companies do with the info that counts. We can use big data as an exercise for insights that lead to raised decisions and tactical business moves.

Big Data - Educational Networks

  • Lots of data is being collected and warehoused
  • Web data, e-commerce data (filter)
  • purchases at department/grocery stores
  • Bank/Credit Card transactions
  • Social Network data

 

How much data are we talking about:

  • Google functions 20 PB per day (……..woooahhh!! that’s too much..)
  • Wayback Machine has 3 PB + 100 TB/month (3/2009)
  • Facebook has 2.5 PB of an end user data + 15 TB/day (4/2009) (…that’s interesting to know…)
  • eBay has 6.5 PB of end user data + 50 TB/day (5/2009)
  • CERN’s Large Hydron Collider (LHC) produces 15 PB per annum

 

Why Is Big Data Important?

The need for big data doesn’t only revolve around how much data you have, but what you need to do with it. You may take data from any source and evaluate it to find answers that allow cost reductions, time reductions, new product development and optimized offerings, and smart decision making. Once you incorporate big data with high-powered analytics, you can attain complete business-related responsibilities such as:

  • Determining root factors behind failures, problems, and flaws in near-real time.
  • Generating coupons at the idea of sale predicted on the customer’s buying patterns.
  • Recalculating complete risk portfolios within minutes.
  • Detecting fraudulent habit or patterns before it influences your organization.

 

Now how does it Work:

Before discovering what big data can work for your business, you should comprehend where it originates first.

Streaming data

This category includes data that extends to your IT systems from an internet of linked devices. You could examine this data as it occurs and can make decisions on what data to keep, what never to keep and what requires further evaluation.

Social media data

The data on public connections can be a progressively attractive group of information, particularly for marketing, sales, and support functions. It’s  in unstructured or semi-structured varieties so that it poses a distinctive obstacle as it pertains to usage utilization and evaluation.

 

Big data has been defined by the three Vs:

They are as follows-

  • Volume (Scale):-

The quantity of data. While volume level signifies more data, it’s the granular characteristic of the data that is exclusive. Big data requires handling high amounts of low-density, unstructured Hadoop data—that is, data of unfamiliar value, such as Twitter data feeds, click channels on a website and a mobile app, network traffic, sensor-enabled equipment recording data at the speed of light, and plus much more. It’s the job of big data to convert such Hadoop data into valuable information. For a few organizations, this may be tens of terabytes, for others, it could be hundreds of petabytes.

 

  • Velocity:-

The Data streams are at an unparalleled velocity and must be handled regularly. RFID tags, detectors, receptors, sensors, and smart metering are generating the necessity to deal with torrents of data in near-real time. These are the things that also has an impact on the internet of things, the coming technology.

  • Variety:-

Data will come in all sort of types – from organized, numeric data in traditional databases to unstructured text contents, email, video tutorials, audio tracks, stock ticker data and financial trades. These are the varieties of data that we are talking here. It can be anything. Any raw content can be the data.

 

Some Make it 4V’s:

Big Data - Educational Networks

 

Big Data Challenges:

  • Capturing data
  • Duration
  • Storage
  • Searching
  • Sharing
  • Transfer
  • Analysis
  • Presentation

 

Big Data Analytics:

  • Big data is more real-time in character than traditional Data Warehouse applications.
  • Traditional Data Warehouse architectures aren’t well-suited for big data applications.

    Big Data - Educational Networks
    Big Data – Educational Networks
  • Shared nothing, parallel processing massively, scale-out architectures are well-suited for big data applications.

 

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