What makes GPU databases ideal for BI? That’s what a new white paper from SQream DB wants to explain — incorporating real-world use cases to highlight how you can turn your existing BI pipeline into “a more capable, next-generation big data analytics system.”
The new report begins by touching on how to gain an edge in analytics as the concept of big data becomes obsolete and data stores reach massive new proportions — as well as how important this really is.
Today’s businesses are more and more leveraging their growing data stores to find valuable insights that speak to them not only on the concept of customer behavior, but also security anomalies, risk analysis, stock and inventory predictions and more.
And as most industries become more and more reliant on their growing datasets to dictate decisions for end-users, many have chosen to store their data in large data lakes. The report contends that often, these platforms that analyze the data “struggle with the velocity and variety of data.”
And that challenge is getting bigger as enterprises’ need for flexible, analytics continues to escalate.
SQream DB contends, “Hadoop, NoSQL, and legacy platforms do not offer the level or analytics flexibility allowed by a full SQL analytics platform.”
Take this, for example. For most SQL-on-Hadoop query engines like Hive and Phoenix systems, only a subset of features is available for understanding your data, according to the report. And further, some NOSQL solutions require you to write customer code; a time-intensive proposal.
Organizations worldwide are facing the challenge of effectively analyzing their exponentially growing data stores. The term ‘Big Data’ is becoming obsolete as we face data stores of massive new proportions.” — SQream DB
In light of this challenge, many turn to OLAP cubes, and pre-aggregated tables, which according to the company have become common solutions for slow SQL queries. But, there’s a problem. This strategy can severely hinder BI.
Instead, the white paper asserts those in the data industry should consider focusing on becoming an analytics-driven enterprise with the help of GPU-powered analytics engines.
The white paper explores how GPUs and GPU databases can enable your data scientist to make better informed decisions, be more productive, and gain insight into your growing data sets.
Download the new report, “Accelerating SQL and BI Analytics: Extending Analytical BI with a GPU Database,” courtesy of SQream DB, to learn more about how GPUs and GPU databases can help you organize and benefit from your next big data analytics system.
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