This vendor-written piece has been edited by Executive Networks Media to eliminate product promotion, but readers should note it will likely favour the submitter's approach.
Today, with the availability of modern hardware profiles, the demands of real-time and interactive data are met - allowing the ability to capture, integrate and incorporate more data points from streams, external sources and internal core systems than ever before. We have no doubt made tremendous progress compared to just a few years ago when we were focused on deploying purpose built analytic databases to meet the defined demands of the data.
With this shift, users are no longer managing against a scarcity of resources, but are instead empowered to build new functionality that increases their organisation's ability to better apply data analytics to a variety of sectors of the business.
Apache Hadoop has already delivered on the promise of limitless analytics by providing a distributed framework, allowing for the collection of massive amounts of data that scale outside the scope of most analytic database environments.
Furthermore, Hadoop has increased analytic performance by removing many common resource bottlenecks by bringing compute resources closer to the data. Hadoop users also now have a choice of processing frameworks and file systems to meet the discrete demands of their use case, without the need to employ multiple technology solutions.
The move towards becoming actionable in response to data is becoming an increasingly powerful ability. Real world examples of how Hadoop has changed our ability to deliver analytic value include helping retailers providing real-time offers through recommendation engines and enabling rapid location based targeting from mobile sources. This is reshaping how marketers target their customers and shape their future product development.
In healthcare, we have seen hospitals leverage time-series data to better understand data from bedside monitors. By feeding this time-series data into an analytics environment, medical staff are able to achieve near real-time event monitoring during surgery and recovery.
The realisation of these new capabilities also brings about the aspiration to leverage even more types of data. We recently see much innovation around solutions for streaming and online data formats, as well as, an increase in analytic tooling that is opening up developer access of the new data types.
Some data types have remained somewhat challenging to analyse. These include the complex nature of rapidly-changing or 'mutable' data types. Here are some examples of the requirements that mutable data demands of analytic systems:
· Time-series data -- where users need to insert, update, scan, and lookup capabilities to address use cases such as real-time streaming.
· Stock Market Data -- where users need to run analytics on a full data set while new information streams in real-time updates.
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