Understanding fast data in big data

Fast data in big data refers to the application of big data analytics to smaller data sets. This can be done in near-real or real-time in order to solve a problem or create business value.

As data is getting collected at an increasing pace, and since most of this data is live, the benefits of big data can be lost if information is not processed quickly. Processing ‘fast data in big data’ at a breakneck speed requires two things: a system that is able to handle developments as quickly as they appear and a data warehouse that has the capability of working on each item once it arrives. These velocity-oriented databases can easily support real-time analytics and complex decision-making, thereby effectively processing a relentless incoming data feed.

Capturing value from fast data in big data

One of the best ways of capturing the value of incoming data is to react to it the instant it arrives. If you are processing incoming data in different batches, you've already lost time, and consequently the value of that data. For processing data that is arriving at tens of thousands to millions of events per second, you will need two technologies: a streaming system that can deliver events as fast as they come in and a data store that can process each item as fast as it arrives.

Goals of fast data

The main goal of fast data is to quickly gather and mine structured and unstructured data so that quick action can be taken. As the flood of data from devices, sensors, actuators, and machine-to-machine (M2M) communication in the Internet of Things (IoT) continues to grow, it becomes critical for organizations to identify which type of data is time-sensitive and should be acted upon instantly.

Potential use cases

The concept of fast data plays an important role in native cloud applications that require low latency and depend upon the high I/O capability that all-flash or hybrid flash storage arrays provide. In the next few years, it is expected that some fast data applications will rely on rapid batch data while others will require real-time streams.

Potential use cases for fast data include:

• Smart applications that can help in analyzing real-time electric power usage at tens-of-thousands of locations. It will also automatically initiate load shedding to balance supply with demand in specific geographical regions.
• Development of smart window display applications that can effectively identify a potential customer’s demographic profile and thereby, generate a discount code or other special offers for the customer.
• Smart surveillance cameras that can record events continuously and can use predictive analytics for identifying security anomalies as they occur.

As fast data boosts your ability to gain insights from data the moment it is generated and empowers you to make in-the-moment decisions, its implementation can enhance the value of your big data initiatives.