Exasol Debuts “Compromise Free” Analytics Database

Exasol is launching its “no-compromise” analytics database, promising to deliver more productivity, savings, and flexibility for enterprises to better manage data in the cloud, SaaS, on-premises, or hybrid.

According to the company, the database offers processing times up to 20 times faster than any other analytics database. Exasol provides an unmatched price/performance ratio, helping customers achieve 320% ROI in reduced licensing, implementation, maintenance, and training costs, according to the vendor.

With the latest enhancements, Exasol seamlessly integrates with any data stack and analytics ecosystem, and dynamically scales to accommodate even the most complex data sets, removing friction and unburdening data teams to accelerate business outcomes.

“Exasol believes customers shouldn’t ever have to make compromises with their analytics databases, especially during these times of economic uncertainty and reduced IT budgets. This is why our offering allows users to see significant performance and efficiency gains, while working within their budgets and existing tech environments,” said Joerg Tewes, CEO of Exasol. “We have hundreds of global customers using Exasol with extremely complex data, at scale. From financial services and retail customers reducing queries from hours to seconds, to agriculture firms working with complicated models supporting DNA sequencing, our customers spend more time analyzing and optimizing with less time and headcount.”

 The latest enhancements to the database enable organizations to:

  • Avoid replacing databases and get more out of existing tech stacks: Through Exasol’s performance enhancements, scaling optimization, and real-time processing, their entire tech stacks are more efficient.
  • Gain the best price/performance ratio: Market-leading concurrency, fast in-memory processing and query compute distribution provides greater performance on less hardware infrastructure.
  • Run analytics or machine learning (ML): Exasol's ML capabilities are built directly into the in-memory database engine, to deliver even greater efficiencies and cost savings.
  • Manage data where it lives: Workloads can be moved between platforms and uniformly automated and managed through a modern technology stack, whether it’s between self or fully managed SaaS, even on an ad-hoc basis.

For more information about this news, visit