Data Governance — Entidade

blog.dropbox.com·há 7 dias

How the people building AI at Dropbox use AI themselves

Whether it’s writing tests, fixing bugs, tackling tech debt, or accelerating migrations, this is how engineers are using agentic AI to build the future of Dropbox—and create more space to do impactful

doris.apache.org·há 16 dias

Data Lineage Technical Guide - Apache Doris

Collect table-level and column-level lineage from supported Doris DML statements through an external LineagePlugin.

ataccama.com·há 19 dias

Closing the lineage gap: how Ataccama and Rocket Software deliver true end-to-end lineage

Data lineage has become one of the most critical capabilities in modern data management. It shows where data came from, how it was transformed along the way, and where it ultimately lands. That makes

tealium.com·há 20 dias

The Rise of the Private Cloud Option: From Edge Case to Expected Choice

For a while, the industry narrative was simple: shared, multi-tenant SaaS won, and private cloud was a legacy compromise you made only if you had no other option. That story is changing. In conversati

ataccama.com·há 20 dias

Snowflake data quality explained: From native capabilities to estate-wide data trust with Ataccama

See what Snowflake’s native data quality tools do well, where they stop, and how to extend quality and governance across your entire data estate. What is Snowflake data quality? Snowflake is a f

blog.dropbox.com·há 21 dias

Protecting your team’s content, wherever it’s stored—so you can safely use AI

AI makes it easier than ever to find and act on information. So how do you ensure that only the right people and the right tools can access your team’s most sensitive content?

blog.dropbox.com·há cerca de 1 mês

Building AI that can search inside videos (and photos and audio too)

Not all work happens in writing. Teams that work with photos, videos, and audio need AI that works for them too.

ataccama.com·há cerca de 1 mês

Data quality monitoring levels 1 and 2: A practical guide to getting started

Data quality monitoring is the continuous evaluation of data against defined expectations for completeness, validity, consistency, uniqueness, and range, paired with the rules, ownership, and escalati