Alation

JUNE 21, 2022

Over time, we called the “thing” a data catalog , blending the Google-style, AI/ML-based relevancy with more Yahoo-style manual curation and wikis. Thus was born the data catalog. In our early days, “people” largely meant data analysts and business analysts. ML and DataOps teams). data pipelines) to support.

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DataOps Data Scientist Data Quality Data Pipeline 52

Precisely

FEBRUARY 10, 2023

Advanced analytics and AI/ML continue to be hot data trends in 2023. According to a recent IDC study, “executives openly articulate the need for their organizations to be more data-driven, to be ‘data companies,’ and to increase their enterprise intelligence.”

DataOps 52

DataOps Data Observability ML ML 52

Alation

JANUARY 25, 2022

This “analysis” is made possible in large part through machine learning (ML); the patterns and connections ML detects are then served to the data catalog (and other tools), which these tools leverage to make people- and machine-facing recommendations about data management and data integrations.

DataOps 52

DataOps SQL ML ML 52

Alation

APRIL 4, 2023

Building data pipelines is challenging, and complex requirements (as well as the separation of many sources) leads to a lack of trust. Troubleshooting data issues , for an exploding number of disjointed systems and tools, breaks self-service for data users and creates gaps in visibility for dataOps.

DataOps 52

DataOps Data Engineering Data Engineering Data Engineering 52