insideBIGDATA

MARCH 28, 2023

In this video interview, Ashwin Rajeeva, co-founder and CTO of Acceldata, we talk about the company’s data observability platform – what "data observability" is all about and why it’s critically important in big data analytics and machine learning development environments.

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Flipboard

SEPTEMBER 28, 2023

million in new funding for its artificial intelligence enterprise observability platform that helps companies keep track of their data costs, usage and performance. Revefi co-founders Sanjay Agrawal, left, and Shashank Gupta. Revefi Photo) Seattle startup Revefi raised $10.5

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Precisely

MARCH 14, 2023

Whatever your unique objectives may be, the Data Integrity Suite’s Data Quality module will play a critical role in your ongoing data integrity journey – ready to help you tackle new use cases with data that’s accurate, consistent, and fit for purpose where you need it most.

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Precisely

APRIL 11, 2023

It’s critical that business analysts have the data they need and that IT has the appropriate metadata associated with those datasets for seamless replication into the cloud. That’s why a data catalog is critical to any organization – particularly if you run analysis and reports in cloud data platforms.

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Precisely

JULY 12, 2024

Data integrity is based on four main pillars: Data integration : Regardless of its original source, on legacy systems, relational databases, or cloud data warehouses, data must be seamlessly integrated in order to gain visibility into all your data in a timely fashion.

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Dataversity

FEBRUARY 4, 2021

Currently, many businesses are using public clouds to do their Data Management. Data Management platforms (DMPs) started becoming popular during the late 1990s and the early 2000s. Click to learn more about author Keith D.

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Alation

FEBRUARY 8, 2022

Signals around the quality and integrity of the data are essential if people are to understand and trust it. Data provenance and lineage, for example, clarify an asset’s origin and past usages, important details for a newcomer to understand and trust that asset. Self-describing. Plane 3: Mesh Supervision Plane.

Data Governance 52

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