Alation

MAY 24, 2022

generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and Data Governance application.

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Smart Data Collective

JULY 15, 2021

You can think of a data catalog as an enhanced Access database or library card catalog system. It helps you locate and discover data that fit your search criteria. With data catalogs, you won’t have to waste time looking for information you think you have. What Does a Data Catalog Do?

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Dataversity

JUNE 19, 2023

In part one of this article, we discussed how data testing can specifically test a data object (e.g., table, column, metadata) at one particular point in the data pipeline.

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Precisely

FEBRUARY 2, 2023

Pipelines must have robust data integration capabilities that integrate data from multiple data silos, including the extensive list of applications used throughout the organization, databases and even mainframes. Changes to one database must also be reflected in any other database in real time.

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phData

AUGUST 10, 2023

The implementation of a data vault architecture requires the integration of multiple technologies to effectively support the design principles and meet the organization’s requirements. Implement business rules and validations: Data Vault models often involve enforcing business rules and performing data quality checks.

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The MLOps Blog

JUNE 27, 2023

Data quality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations. Dolt Dolt is an open-source relational database system built on Git.

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ODSC - Open Data Science

FEBRUARY 28, 2023

Making Data Observable Bigeye The quality of the data powering your machine learning algorithms should not be a mystery. Bigeye’s data observability platform helps data science teams “measure, improve, and communicate data quality at any scale.”

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Precisely

OCTOBER 5, 2023

When we think about the big picture of data integrity – that’s data with maximum accuracy, consistency, and context – it becomes abundantly clear why data enrichment is one of its six key pillars (along with data integration, data observability, data quality, data governance, and location intelligence).

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phData

FEBRUARY 22, 2023

Data Quality Good testing is an essential part of ensuring the integrity and reliability of data. Without testing, it is difficult to know whether the data is accurate, complete, and free of errors. Below, we will walk through some baseline tests every team could and should run to ensure data quality.

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Precisely

FEBRUARY 9, 2024

For instance, a data breach or violation of privacy standards can lead to liability, expensive fines, and a slew of negative publicity that’s a hit to brand reputation and trustworthiness. If inconsistencies and inaccuracies in the customer database can be fixed, the organization’s data analytics initiatives can presumably proceed.

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The MLOps Blog

AUGUST 3, 2023

One of the features that Hamilton has is that it has a really lightweight data quality runtime check. If you’re using tabular data, there’s Pandera. Like they didn’t have to think about, you know, data observability, but look, if you provided those data, we captured things about it.

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Precisely

OCTOBER 12, 2023

Monitoring and improving business quality rules and technical quality rules to define what “good” looks like. Creating data observability routines to inform key users of any changes or exceptions that crop up within the data, enabling a more proactive approach to compliance. Privacy requirements. Intelligence.

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