BLOGS

Today, we’re excited to announce Acceleration 2.0. We have seen great interest and innovation in the big data acceleration market in the past two years, and today’s announcement really takes us into a new era of acceleration. In particular, the expansion of functionality changes the game for investing in hardware acceleration in your data center.  With the combination of caching and acceleration and increased coverage of file formats and use cases, customers can now upgrade their Apache Spark clusters 80% less expensively than in the past.

A growing big data acceleration market is bridging the disconnect between applications and CPUs, adding ASICs, FPGAs, and GPUs for improved performance.

Bigstream can’t solve the chip shortage, but can help solve one of these three challenges.

The adoption of big data analytics in the cloud is well underway. Five to 10 years ago, it was hard to imagine housing and managing large volumes of data in a public cloud. Times have changed, though, and the cloud options have evolved to meet enterprise needs, particularly around performance, security, and reliability.

Although extract, load, and transform (ELT) workloads have been around for ages, they still comprise some of the biggest challenges that Apache Spark™ users bring to us. Like with all applications that process large data sets, ELT performance problems create delayed results, missed deadlines, and even low morale.

IOT pipelines are compute-intensive. Apache Spark performance is often put to the test in these scaled environments. Learn how a Bigstream IOT customer was able to improve its Spark workflow Sixfold.

Nearly every CEO aspires to make their company data-driven, infuse a data culture, and advance along the data maturity curve. If they fail, their board of directors will seek someone who can harness the value of data. Companies and other organizations succeeding on this path have invested in both the skills and culture, but also a fair amount of technology. Data is top of mind, not just for the CEO, but also for leaders throughout the organization.

Every organization wants to be all-in on digital transformation, seeking to dive deep into its big data platforms to drive efficiency and optimization. As these efforts become more critical to your enterprise, the priority becomes building an infrastructure that can meet the demand for a much higher computational power. CPU clustering has been one avenue, but Moore’s law is no longer applicable.

To improve the performance of compute infrastructure and to keep up with the expanding requirements of data analytics and AI, many enterprises are looking to hardware acceleration as a integral solution. In most situations, advanced programmable hardware—mainly GPUs and FPGAs—is the primary source of acceleration. By using this advanced hardware, enterprises are gaining computational advantages; however, there are still reasonable concerns related to the difficulty of programming this specific hardware.