This buyer’s guide lays out key considerations for organizations looking to apply data virtualization to their analytics use cases. Along with key features and capabilities, you’ll learn about the difference between data virtualization and query federation, drill down on caching techniques and get a detailed checklist for evaluating vendors in the space.
Topics covered include:
We also point you to additional resources and further reading to help you as you continue your research.
Learn how to mitigate the challenges of processing huge volumes of diverse and distributed data, including performance and cost.
Understand how to enable BI and Data Science teams to find and use the data that is relevant for their particular analyses.
See why data virtualization and automated data engineering are critical for ensuring that analyses include all the relevant information needed, at the time of need, without taxing IT resources.
Customers who have chosen the right tool for the job — the right virtualization for the data.
AtScale's Adaptive Analytics Fabric: Take command of your data. No matter the scale.
AI-driven autonomous data engineering alleviates complex and time consuming data movement and transformations. Leverage your existing data infrastructure with unparalleled performance and scale in hybrid cloud, multi-cloud and on-premise environments.
Interactive query performance on live data for just-in-time insights. AtScale auto-tunes query performance through user behavior analysis and artificial intelligence for predictability and efficiency in resource consumption.
Ensure data security and governance with business level (semantic) security along with the underlying native database security. Integrate with data platforms’ native security capabilities seamlessly and transparently.