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.

1. How Fast Data Powering
your Real-Time Big Data

2. "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.

3. • 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:

4. 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.

5. • 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.
Data Analytics

6. • 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.
Streaming Data Analytics

8. • 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.
Fast Data Architecture

9. • 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 solution 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.
Fast Data Usage

11. 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:
1. 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.
2. 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.
3. 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.

12. 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.

13. 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 O’Reilly
Media)
Source: Cuelogic Blog

14. About us:
Cuelogic Technologies
Unit 610, 134 W 29th St,
New York, NY 10001
+1 347 374 8437
info@cuelogic.com
https://www.cuelogic.com