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The Dramatic SQream DB Impact on performance and cost efficiency for massive data analytics
Ilan Gross
July 24, 2020
Introduction
When tasked with improving the ability to analyze data, you want a powerful solution that is fast and cost efficient. Let’s discuss the SQream DB difference. Founded in 2010, SQream is an award-winning startup with a presence worldwide.
Data stores are growing exponentially. As data is held for longer periods, complexity increases. Customers are looking for more and new insights, of high quality.
SQream DB has leveraged the power of petabyte-scale big data analytics, offering the most cost effective SQL database on the market.
Customers are telling SQream that they want to be a data driven organisation, but their issue is that they are only analysing a fraction of their massive data due to a few reasons. It could be that their systems were not designed to handle that much data, or the time to prepare and model the data is growing, as are the costs to upgrade their data warehouse.
With traditional SQL solutions, the more data you have, the lower percentage can be analysed. Valuable insights go undiscovered and this affects decision makers directly. That is why SQream DB provides unmatched savings in storage cost and runs on much less hardware than other data warehouse solutions.
SQream condenses the power of the full-rack a standard 2U server, but offering the power of GPU-acceleration.
The reason for this pivot, is the continued advancements in GPU efficiency in chip design and production in recent time.
The challenge with appliances is that they are expensive to begin with, are very costly to scale and are inflexible as they link compute and storage together.
While Hadoop is a great file system, it is not designed to solve Big Data analytics and is a hard to maintain environment.
You want to achieve previously unobtainable business and operational insights? This is what SQream DB was built from the ground up to offer, by eliminating big data processing size, speed and cost limits. By focusing on business insights and not infrastructure, you get the best answers.
By eliminating complex data pipelines, it addresses the poor scaling of traditional soultions.
See here, a client that had traditional big data systems in place which producted Tibco Spotfire reports. In the past, this process took one hour. With SQream DB, only 46 seconds! Imagine the possibilities.
Compact size with huge power and cost effectiveness
By decoupling storage and compute with GPUs, it is very easy to add more power, as a 1U server can have 20,000 cores!
It’s hard to believe, but that 1U server with Nvidia GPUs can replace ~100 terabytes of 42U legacy data warehouse racks!
The SQream cost of performance compared to a Teradata EDW is totally astounding!
You want to achieve previously unobtainable business and operational insights? This is what SQream DB was built from the ground up to offer, by eliminating big data processing size, speed and cost limits. By focusing on business insights and not infrastructure, you get the best answers.
By eliminating complex data pipelines, it addresses the poor scaling of traditional soultions.
See here, a client that had traditional big data systems in place which producted Tibco Spotfire reports. In the past, this process took one hour. With SQream DB, only 46 seconds! Imagine the possibilities.
This is part one of a three part series, continue to part two, or visit our SQream DB Product Page
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Ilan Gross
Ilan Gross
This is part one of a three part series, continue to part two, or visit our SQream DB Product Page
