How Fast Data Powering your Real Time Big Data

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How Fast Data Powering your Real-Time Big Data
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• "Businesses and their users are facing what one
  might call a perfect storm - decision-makers need
  insight faster than ever, and yet IT is
  struggling to avoid becoming a bottleneck."  -
  Jason Stamper, Analyst, 451 Research.
• Fast Data is not a new concept. It has been
  around before Big Data  and IoT came into the
  picture. Data partitioning, data warehousing and
  scaling servers were the steps taken to speed up
  data retrieval prior to IoT and Big Data.
• The writing on the wall Big Data volume in no
  longer the main criteria for gathering quality
  data. Companies are now vying to build better new
  platforms to solve data warehousing tasks and in
  processing analytics.

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• In the modern tech context, Fast Data is about
  information in real-time or the ability to obtain
  data insights while it is generated. That is why
  streaming data is so happening now. Data streams
  now occur at thousands of times per second, what
  is now called Fast Data.
• The truth is, big data services companies still
  don't know what is to be done with it. Most
  companies use Hadoop for their data storage. Fast
  Data origins can be linked to Big Data variety,
  velocity and volume concepts. Fast Data is not
  just about high frequency data intake.
• It is about data processing in real-time,
  arriving at quick action-based results and taking
  decisions based on these results. All this while
  dealing with complex analytics. Conclusively, Big
  Data Services can only be effective if
  organizations interpret Big Data findings in
  real-time

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Data Processing Timeliness

• Picture an online shopping company that wants to
  recommend its products to a customer.
  Recommendations are based on the customer's
  latest purchases. Only, the shopping website
  can't make these recommendations fast enough.
• How soon in real-time can the website collect
  data, summarize and then provide the shopping
  options - preferably in real-time? Unless they
  want to lose the customer. This is where Fast
  Data comes in, adding immediacy to the
  proceedings. Timeliness and accuracy are two
  prime Fast Data attributes.
• Fast Data includes sampled recommendations,
  sensors that pass on instant trend changes and
  choices. When it comes to pinpointing loopholes
  or instances of inefficiency, go for Fast Data.
  View this video to know more about the need for
  In Memory database technology in Fast Data.

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Data Analytics

• More focused analytics is now possible, thanks to
  Fast Data. Analytics enables customization of
  services or products. It enables better
  decision-making, leading to better customer
  service and faster fraud detection, among other
  things.
• The question you need to ask is, at what
  particular time do you go for analytics? The more
  you are able to analyze in real-time, the more
  easier it becomes to take action on the basis of
  analytic results. Learn about Fast with Apache
  Spark in this video.

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Streaming Data Analytics

• Fast Data makes a critical difference in
  obtaining results within a limited time span. For
  example, why would you want information on a
  customer who has already left the store or
  website? Fast Data helps organizations make
  similar make-or-break decisions.
• Processing streaming data is a vital part of Fast
  Data. Making automated decisions based on
  streaming machine data is important for the
  process. You may call this streaming analytics.
  At the same time, human intervention in the
  automated decisions are necessary.
• That is why the automated dashboards and
  streaming data sources need to be interactive for
  that ever important human tweaking and final
  authorization.

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Fast Data Architecture

• When we look at a Fast Data architecture, it will
  feature real-time analytics, taking in
  information, and giving immediate results and
  resultant decisions.
• Instant, real-time solutions are possible if you
  integrate your Big Data system (consisting of a
  Hadoop database, SQL on Hadoop, MapReduce and
  related big data components) to the company's
  applications.
• This whole set up can then be connected to the
  Fast Data architecture as displayed in the
  illustration above.

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Fast Data Usage

• Elements like dashboards can be served quickly,
  with Fast Data usage. The operations systems can
  be constantly powered by instant analytics, the
  entire system thus working at a rapid pace.
• Building this big data dependent applications
  with big data services providers combined with
  fast data capability applications can entirely
  change its efficiency.
• Architecture plays a key role here. Learn about
  picking the right database for Fast Data here.

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The Emerging Big Data (Fast Data) Stack

• Finally, Fast Data is Big Data that is constantly
  moving. Imagine a pipeline through which data is
  flowing in great speed. Here are the Emerging Big
  Data (Fast Data) Stack details
• The first level concerns focused services. It
  concerns applying key processes and functions to
  obtain significant value from streaming data.
  Fraud detection, travel forecasting and similar
  services can thus be availed faster.
• The second layer consists of real-time analytics
  based on the streaming data. The company's
  business logic is then put to use to make
  real-time decisions.
• In the Fast Data layer, the data is then exported
  for analytics and long term storage to Hadoop and
  other data storage options. Speed, real-time and
  accuracy are key elements of the entire stack.

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Streaming is however just a part of the Fast Data
solutions. OLTP databases are the in thing
for processing streaming data. You can thus have
speed and scale using an in-memory database,
designed to handle data streaming at great speed.
One popular Fast Data database is VoltDB.
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Summary

• Fast Data is powering innovation, while using Big
  Data to obtain key insights and conclusions.
  Anything real-time, be it security, fraud
  surveillance, risk analytics, customer choices,
  etc - Fast Data helps deliver instant, accurate
  Big Date solutions. The Big Data and Fast Data
  challenge is finally about concurrency.
• Just how much amount of data can be taken up at a
  given amount of time? This is for companies to
  decide. Read more about technology in our blogs
  section.
• (Emerging Big Data Stack and Fast Data
  Architecture Images, Courtesy OReilly Media)

Source Cuelogic Blog
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