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SQream Technologies Sales Director on Using a GPU Data Warehouse for Analytics

Scott Geissler, Sales Director of Midwest Region at SQream Technologies, offered insights into the use of graphic processing unit (GPU) data warehouses for analytics during his presentation at the 2018 Leadership in Big Data & Analytics Forum in Chicago on December 4. In his presentation, “GPU Data Warehouse for Massive Data Analytics,” Geissler provided tips to help organizations leverage GPU data warehouses to optimize their everyday analytics.

Today’s organizations want to collect as much data as they can and analyze it as quickly as possible. Yet accomplishing this goal often proves to be a major struggle for organizations of all sizes and across all industries.

Oftentimes, organizations leverage central processing unit-based (CPU-based) data collection and analysis tools. Although these tools allow organizations to collect and assess data, they sometimes can be difficult to implement into an organization’s day-to-day operations. Also, CPU-based data collection and analysis tools rarely help organizations quickly and effectively transform massive amounts of data into meaningful insights.

“We’re awash in all of this data. We have needle-moving information all around us, all the time. But we’re struggling to get to [this information] because we can’t analyze all of it,” Geissler said. “We want to make it so we can analyze much more data, much faster.”

Now, GPU-based data collection and analysis tools could prove to be difference-makers for organizations. These tools help speed up data collection and analysis, as well as ensure that organizations can gain the insights they need, precisely when they need them.

GPU technology allows organizations to meta-tag data – something that is unavailable with traditional CPU-based data collection and analysis tools. Therefore, GPU technology frequently helps organizations accelerate and improve their data analysis.

“When we bring data in, we meta-tag it,” Geissler pointed out. “This allows us to skip over unnecessary columns of data, thereby leading us to data that is only relevant to that query and helps save time on the analysis of that query.”

Additionally, GPUs help organizations keep pace with the rapidly increasing amount of structured and unstructured data available. They ensure that organizations can constantly collect data from a broad array of sources and use this information to obtain the insights they need to keep pace with industry rivals.

“Data volumes continue to proliferate unabated,” Geissler stated. “And the resiliency of flash storage and networking speed to flash has improved dramatically.”

GPUs are proven to perform at much faster rates than CPUs, too. They can perform quick calculations that enable organizations to retrieve meaningful insights without delay.

Typically, GPUs can perform the same calculations as CPUs in a fraction of the time. GPUs are better equipped than CPUs to handle substantial data volumes without requiring an organization to invest additional time and resources into data analysis. Thus, GPU data warehouses are becoming must-haves for organizations that want to speed up and improve their data analysis.

“GPUs are much faster than CPUs,” Geissler indicated. “When you have a compute-intensive operation … you have relational algebra. And GPUs move really fast and crush CPUs in terms of calculations like that.”

Organizations can use GPU technology to streamline data ingestion as well.

Whereas organizations often are forced to deal with data ingestion limitations due to CPUs, GPUs help eliminate these limitations. GPUs typically provide faster data ingestion in contrast to CPUs, and they enable organizations to seamlessly generate actionable insights hidden with large data volumes.

“Data ingestion is exceptionally fast and efficient in a GPU environment,” Geissler pointed out. “We see average ingestion rates of 2 to 4 terabytes per hour with GPU.”

In terms of data exploration, GPUs can be exceedingly valuable. GPUs ensure organizations can assess different types of data, any time they choose. Furthermore, they allow organizations to compress data as needed.

“[GPUs] enable near real-time exploration of your data and your raw data,” Geissler noted. “And GPUs are outstanding for processing … and data compression.”

Data scientists also can leverage GPUs as part of their day-to-day activities. GPUs empower data scientists with unprecedented flexibility relative to CPUs. In fact, GPUs may help data scientists bolster their everyday productivity and efficiency.

“Being in a GPU environment enables you to have a larger sandbox for data scientists to play in and validate their models,” Geissler stated.

As organizations explore ways to get the most out of data, GPUs may be beneficial. GPUs help organizations speed up the process of identifying and analyzing industry and consumer patterns and trends. As such, building a GPU data warehouse may empower organizations to gain the insights they need to accomplish their immediate and long-term goals.

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