PrivOps saves you on Lakehouse, cloud and headcount costs

 

  • Cloud vendor SW licenses

  • Cloud vendor infrastructure

  • Automation and usability

  • Number of headcount

  • Cost per headcount

 

What is a Data Fabric?

An architecture and set of data and application services

  • Across legacy and modern systems

  • That span hybrid multi-cloud environments

How is the Data Fabric different?

The PrivOps Matrix™

  • Data Integration as Code

  • Real-time, dynamically re-composable data flows

The award winning PrivOps Matrix™: A hot-swappable backplane for building Data Fabrics

The PrivOps Matrix™ creates a modular scaffolding for integrating custom, open-source, and proprietary components that interact with data and applications. The result is a hot-pluggable, best-of-breed enterprise architecture that automates the construction and management of data pipelines.

Get control back from proprietary cloud and IT tools vendors by owning your data. You choose what tools to use, switch vendors as needed, accelerate integrations and automations with modular, reusable components, and reduce cost with an integration approach that scales.

Matrix Architecture

Imagine being able to reconfigure data pipelines automatically based on security breaches, changes in cloud pricing, data location, application performance, requestor identity or a host of other situations. Each data pipeline is composed at run-time based on predefined policies. This makes it possible to reuse and share data pipeline components across data pipelines as well as reconfigure data pipelines in real-time. As the data pipeline operates, a decision engine loads different application policies and data dependent on external events and state. This takes data pipeline adaptability to a new level.

Data On-demand

Any data, anywhere

Create tens, hundreds, or thousands of data pipelines to create a virtual view of your entire organization’s information. Easily share data across organizations. With the PrivOps Matrix™, you extract & process the data only when you need it. As a result, you only touch 5-10% of the data you would have stored in a traditional data lake and reduce infrastructure costs by not needing to process, replicate, or transmit the 90-95% of data you don’t need.