What is Reactive Data and why do I care?

  • All the cool kids are doing it!

    All the cool kids are doing it!

    Crunching more data of all kinds, faster and cheaper without using databases. The writing's on the wall, as data volume and variety increase the digital filing cabinets we grew up with are not keeping up with modern application demands. Although new technologies emerged to solve the so-called Big Data problem, many apps are bypassing databases altogether and using new techniques for in-memory computing to work with large volumes of data on the move. Because let's face it, today's BIG DATA is just tomorrow's data. Welcome to the new normal.
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The big challenge with enterprise data is the rate at which new information is being created by modern apps and devices - the so called data velocity.  Storage and query mechanisms pioneered by database vendors decades ago are no longer sufficient for timely data analysis;  and must often be combined with other techniques. To get the job done some folks are taking a different approach.

Instead of loading raw data into a purpose-built database, it is moved unchanged into commodity storage devices (sometimes called Data Lakes), eliminating the up-front cost of data ingestion and preparation. In-memory analytics services then move the data around, creating aggregate views as needed. The resulting architecture pushes more data into application memory, improving performance, flexibility and providing new, powerful ways to query it.

In-memory analysis is also gaining popularity because computer hardware itself is changing. While data volume and velocity are increasing, Moore's Law, the observation that processor speed will double every few years as cost decreases, is no longer true.

As processors get smaller, its getting harder to boost their power in a cost-effective way. Instead, chip makers are focusing on parallel processing. This means solving performance problems is not as simple as buying faster machines anymore. Data crunching systems must be adapted or re-written to take advantage of new hardware. Offloading data processing into application memory lets developers do just that.

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Data velocity may be transforming application architecture but there is another good reason for pushing more data into the apps:   the way we are using data is changing. You hear it everywhere. 'Notify me when it ships'.. 'Sell when it hits the limit'.. 'Text me when it gets there'.. Technology being called to action in response to changing data.

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Modern application content comes from many sources and is much more personally relevant. It's arrival, presence or abscence is often just as important as the data itself. Decision support systems are evolving into Data Processing Networks capable of capturing changes and turning them into actions. In-memory analytics enable proactive, data-driven decision making by letting users capitalize on vast new sources of fast-moving data. We call this Reactive Data Processing.


Reactive data processing lets application developers, data analysts and scientists make use of new query and computation techniques like Stream Processing and Reactive Programming by making them part of the data processing language. Reactive means that a data management system is smart enough to see, manage and react to changes and turn them into actions. This lets data change drive application logic, allowing developers to build a new breed of proactive systems that power innovative technologies like Trade Automation, Internet Bots and Predictive Analytics.

The reactive data layer lets dependencies between elements be configured (not programmed), allowing changes in source data to be automatically applied to all dependent data collections, regardless of where they are located. It is ideal for handling large volumes and variety of transient data, giving users and DevOps teams
a tool for working with big data in-motion.

Reactive data technologies seamlessly blend traditional analytics, stream processing and asynchronous data crunching techniques, making it easy to work with constantly changing data on-the-move. The Reactive Data Fabric™ allows users to do this in a cost-effective manner, taking advantage of new hardware and data query features to enable real-time, data driven decision making at scale.


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