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In 2016 our team of industry veterans began building a hyperscale data warehouse to tackle large, complex workloads.

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Published June 2, 2022

Big Data, Big Changes: How Hyperscale Data Analytics is Shaping the Future

Director of User Experience Jonathan Kelley

By Jonathan Kelley, Senior Product Manager at Ocient 

The amount of data stored by companies around the world continues to grow at an unprecedented rate. This data explosion has created new opportunities and challenges as companies work to put big data analytics to work to help them make better business decisions in every aspect of their business.

To meet these challenges, it’s critical to develop next-generation solutions that set new benchmarks for query processing delivering substantially higher performance than other offerings currently on the market without drastically increasing costs. In other words, just because you want to double or triple the size of data analyzed, it doesn’t mean you can double and triple your investment.

So how exactly is hyperscale analytics for big data paving the way for better data analytics? How does it benefit the modern company that needs to process and analyze massive amounts of data at an affordable cost? Let’s further analyze the challenges presented by ultra-large data and how hyperscale data analytics is shaping the future.

Big Data, Big Changes

Today, terabytes to petabytes of data are common in a hyperscale data center, but that data often remains difficult to analyze in a meaningful way. This year, Google reported that its data centers were handling about 1 million requests per second (RPS) and were processing more than 20 petabytes of data per day—and that’s just from search queries. At times like these, it can be difficult to know where to start analyzing all of that valuable information, let alone how to do it quickly. Even if you find what you’re looking for in your overwhelming amount of data, it can be difficult to make sense of it.

Hyperscale computing has been crucial to many industries from gaming to oil and gas exploration to detecting financial fraud. But when it comes to hyperscale data analytics, not every organization has the resources of a tech giant like Google, and measuring performance and cost-efficiency may therefore be quite challenging. Handling petabytes of data without sacrificing performance is not the simplest feat, but that doesn’t mean that there aren’t solutions. Today, companies like Ocient are tackling hyperscale data analytics to help companies make sense of their data and gain the insights they need to stay ahead of an ever-changing world powered increasingly by data.

How Hyperscale Data Analytics Is Changing the Game

One area that we see being disrupted dramatically by next generation hyperscale solutions is analytics for big to ultra-large data. This trend is fueled by growing aspirations to tap into valuable insights that massive scale data can provide to influence and inform marketing, develop new products and services, and enhance public safety.

For example, vehicle manufacturers could better understand driving patterns and behaviors to develop new in-car entertainment services or determine where to place their next dealership.  Intelligent disaster response teams may leverage hyperscale data analytics to better predict earthquakes through patterns in social media usage. The opportunities are limitless and demand for these kinds of capabilities will only increase as more individuals and companies jump into cloud computing and more 5G-enabled devices come online.

As data continues to explode, hyperscale data centers have emerged as one of three major data center market segments. Their use cases aren’t limited to web-based applications, either; hyperscale data centers can be used for a wide range of computationally intensive tasks like simulations and modeling.

But how exactly does hyperscale data analytics support users today? Hyperscale data analytics for big data enables users to run complex queries across billions of records in seconds using software-as-service (SaaS) or on-premises software. The solution gives business users access to vast amounts of information through new visualizations while giving IT departments an easy way to protect sensitive data.

The future of hyperscale data analytics relies on leveraging high performance industry standard hardware, high core count processors and ultra-fast networking. At Ocient, we’ve developed a Compute Adjacent Storage Architecture (CASA) optimized for high throughput NVMe solid state storage to deliver orders of magnitude improvements over conventional systems. The ability to process petabytes of data without the typical bottlenecks of traditional data warehouses enables faster queries and better real-time analytics performance.

Another major benefit? The cost! When you think about ETL, storage, and analysis being provided to you in such a way that you can tackle analytics for big data and search trillions of data points ten to 50 times faster than normal, you might think that this type of development would be extremely costly for your organization. Instead, modern hyperscale computing solutions like what we have developed at Ocient are cutting costs for organizations while enabling them to analyze more data from an increasing number of diverse and disparate sources.

Of course, the points on hyperscale data analytics provided above only begin to scratch the surface. Using the right technology offered beyond the hyperscale data center market, you can make your data easier to load and stream directly in seconds without sacrificing performance along the way. It’s also important to leverage a solution that can execute new queries to uncover insights that weren’t possible before given the sheer amount of computing power required to process ultra-large, hyperscale datasets. And the ability to maintain flexibility and accessibility, such as employing standard query and analytics interfaces like SQL and JDBC or ODBC while manage workloads more effectively, maintaining security, maximizing performance, and reducing the total cost of ownership (or TCO) are all critical to delivering an effective, impactful solution at hyperscale.

Analytics for big, ultra-large data might have, at one time, felt like something completely unachievable. But with Ocient’s hyperscale data analytics solutions, it’s not only easier than ever to process large collections of data, but to analyze them and receive actionable insights in a matter of seconds in a cost-effective manner. With the support of the right hyperscale data analytics solutions, you can leverage the wide range of benefits outlined above to help your organization get the most out of the data it collects – regardless of whether you are in telecommunications, AdTech, operational IT, a government agency, or other organization.

Tap Into the Power of Your Data With the Support of Ocient

Founded in 2016, Ocient was designed to deliver hyperscale data analytics for big data to help modern enterprises derive value from the trillions of data points that they’re currently struggling to analyze. A modern solution for ultra-large data issues, Ocient focuses on providing the best possible results through a high-performance hyperscale data warehouse that scales without limits to give your organization the support and flexibility it needs. Ocient’s data analytics solutions offer better storage with a smaller footprint, which naturally cuts costs, and provide users with an end-to-end solution that meets the needs of their business requirements and use case with no extra tools and no hidden costs to perform the functions you need.

Contact us now or schedule a demo to get started.