data analytics ICT
and SAS who invariably work in conjunction with platforms provided by third party DBMS vendors and Hadoop distributors, although SAS in particular is blurring this line with growing support for its SAS- managed in-memory data grids and Hadoop environments. Other vendors focused on NoSQL, such as, 10Gen, Amazon, CouchBase and Neo Technologies, and NewSQL vendors are heavily focused on high scale transaction processing rather than analytics.
Looking forward, advances in bandwidth, memory, and processing power also have improved real-time stream processing and stream- analysis capabilities, but this technology has yet to see any wide adoption – this is however definitely a space to watch for the future...
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Big Data is often talked about in terms of the software solution, but presumably there’s more to data analytics than installing an application and off you go?
Other important considerations in establishing whether your Big Data environment is capable of delivering, other than the applications themselves, are: £ The underlying storage – is it fit for purpose? £ What special requirements do servers need; and £ Is your network up to the job?
For example, presumably end users can build their own Big Data solution, or purchase one, more or less, off-the-shelf?
A
Yes they can, but I would like to think that we have helped them in their decision and steered them towards purchasing the right solutions for them.
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Q A
In either case, presumably the hardware infrastructure supporting the Big Data application needs to be ‘fit for purpose’?
Big Data analytics may seem to be an IT ‘wonder drug’ that more and more companies believe will bring them success. But as is often the case with new treatments, there’s usually a side effect – in this case, it’s the reality of current storage technology. Traditional storage systems can fall short for both real-time big data applications that need very low latency and data mining applications that can amass huge data warehouses. To keep the Big Data analytics beast fed, storage systems must be fast, scalable and cost effective
Digging deeper, what are the requirements for storage in terms of supporting a data analytics application?
Storage for supporting data analytic applications differs dependent upon whether there are synchronous [real-time] or asynchronous [batch] processing requirements. In real-time use cases, speed is a critical factor, so the big data storage infrastructure must be designed to minimize latency. Solid state devices are consequently popular options for handling real-time analytics. Flash storage can be implemented in several ways, as a
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