Why streaming analytics?

Why streaming analytics?

Why streaming analytics? The short answer:

because the traditional way of data analytics is not good enough for streaming data.

But of course there is more to it!

Definition of streaming analytics

Streaming analytics is the ability to continuously analyze streaming data, on the fly. The analysis can be in the form of mathematical calculations, statistical analysis, image processing, packet inspection, etc. Streaming analytics enables analysis of data in motion.

Traditional way of data analytics

Data analytics originated with companies analyzing their historical data. Data was typically stored in data warehouses during the day and analyzed overnight. This way of analyzing “data at rest” is fitting for, for instance, slow moving consumer goods and stocks that are refilled twice weekly.Unfortunately, the traditional way of data analytics is completely inadequate for analyzing so-called time-sensitive or perishable data, or if you like your insights now.

Why change?

For some businesses and processes it is crucial to analyze and take action as soon as data arrives to obtain business value. Data can be incredibly valuable at the instant it comes in, yet next to worthless if you don’t act on it at the same instance. In that respect, perishable data is similar to perishable goods – if you don’t use it in time, you might as well throw it away. Streaming analytics addresses this need for timely or instant analysis of streaming data.

Electronic stock trading is a well-known example for streaming analytics, as fast decision making on incoming data can make the difference between huge profits and losses. Factory floor monitoring to predict imminent machine failure is another example of a streaming analytics application.

Streaming analytics: from data to action in a split second

In many applications, data comes in continuously, in data streams of sensor data, factory data, communication data, etc. The data stream continuously refreshes and changes. If time to insight is crucial, data aggregation and subsequent batch data processing is simply too slow.

Moreover, often data arrives at a rate that makes it impossible to store everything in memory. It is then more useful to filter out and store what you are interested in, and throw away irrelevant data.

Streaming data requires a different approach for real-time analysis, called streaming analytics. Hardware and software filter and analyze streaming data on the fly, the instant it streams into the platform from a myriad of different sources. Streaming analytics allows real-time extraction of the information that resides in the data streams, and powers real-time decision making. From data to action in a split second!

Like to know what FlexaWare can do for streaming analytics? Read more...