SQream Blog – Let’s Talk Big Data

Big Data’s Rocket Fuel: GPUs Help Revolutionize Customer Behavior Analysis

As today’s customers come to expect a personalized experience when interacting with a business, customer analytics is expected to become the backbone of the customer journey, creating touch points at every level of the funnel and at every moment of interaction. Today, technology experts are predicting that analytics will do more than ever to drive customer satisfaction. As such, GPU-powered Big Data has arrived, in a big way.

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Meet the Supercharged Future of Big Data: GPU Databases

Big Data systems are built to handle data intensive applications. Now, as large-scale machine learning and streaming start to play a larger role in the enterprise, the Big Data systems are in need of more computational capabilities. Leveraging GPUs for analytical workloads is on the rise, particularly among telcos, ad-tech companies, financial services, and retail organizations that often deal with extremely large data volumes with high scaling and real-time processing requirements.

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CPUs and GPUs – There’s enough room for everyone

GPUs are the hottest trend, and everyone wants in.
We see the hype and expectation, and we understand them – but the GPU isn’t magic. For many, it is just a brute-force, multi-core processing platform. Yes, it can do many things quite well, but it can’t do everything. We must talk about it, because we are doing both CPU and GPU a disservice by ignoring it.

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Big Data’s Big Three: Business Executives, Data Scientists and IT Leaders

It is the IT leaders’ responsibility to make sure that data scientists have the technology and infrastructure required for the latter to be able to deliver actionable insights to C-level executives. In turn, these business leaders will use this information to decide upon which strategies to pursue. The selection of analytics technologies is crucial – making speed a differentiator, not to mention cost, and exploiting value in all types and scales of data. This requires an infrastructure that can manage and process exploding volumes of data, without becoming an IT-focused entity as a result of complex implementations or overly intricate management of Big Data analytics technologies.

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Data Scientists, Got a Clue about GPU? You Really Should…

Until recently, extracting insights and information from data meant having an in-depth knowledge of SAS or R and sklearn, as well as being familiar with data processing frameworks like Spark.
However, primarily due to the emergence of GPU computing, we now have a lot more power, with less required hardware to run a query. Besides enhanced capacity by orders of magnitude, GPUs perform matrix operations that are quite conducive to running back propagation computations in neural nets. It’s this rise of neural network in data science that’s feeding the demand for smaller supercomputers, like GPU-enabled servers.

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