SQream Blog – Let’s Talk Big Data
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.
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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What is the Fastest Way to Scan 100 TB of Data?
Say you have 100 TB of data in a table with a year’s worth of information in it. Your data is partitioned by a key, and ordered by date. Your manager asks you to quickly answer a whole lot of questions about last year across all regions, regardless of the key. And it has to be on his desk, fast. Any thoughts? How do you approach this?
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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.
Ad Campaigns, Optimized: Big Data Helps DSPs Translate Insights into Action
Not long ago, marketing teams would spend days analyzing mounds of Excel documents that contained campaign data, reading the tea leaves in an attempt to connect desired actions to the right customers. Today, Big Data platforms are helping optimize DSP efficiency, which enhances the information available to media buyers and increases ROI. Such a streamlined process enables advertisers to spend their budgets with a higher likelihood of conversion and publishers to earn the most money on their inventory.
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