Alation partners with Bigeye and Soda

Alation’s interoperability provides customers with choice of leading data observability vendors.

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Alation has announced formal partnership agreements with Bigeye and Soda, leading data observability vendors. The partnerships provide customers with the choice to select the best data observability partner for their organization’s needs.

Alation’s active data governance helps organizations drive data culture by providing all data users, regardless of technical skillset, with access to quality data. Bigeye’s data observability platform helps detect data quality issues and ensures reliable data pipelines. Soda’s data observability platform allows teams to discover, prioritize, and collaboratively resolve data issues. The platforms are invaluable for alerting technical users to deeper data quality issues, such as the trustworthiness of the data or its usability. Alation’s integrations with Bigeye and Soda provide all data consumers, from business users to data scientists, with a single-pane-of-glass view into the trustworthiness and reliability of data. This results in a streamlined analysis process that empowers data users to use data and generate insights with confidence.

“When it comes to data intelligence and data governance, Alation has long catered to the needs of every user, not just the technical user. Our customers see Alation as the primary source of reference for all metadata, which enables all teams across an organization to make data-driven business decisions,” said Raj Gossain, Chief Product Officer, Alation. “The Alation Data Catalog is a platform known for its interoperability. With our APIs and partnerships, we continue to expand the ecosystem of companies that work with Alation, giving our customers a modern user experience that seamlessly integrates best-of-breed solutions that meet their needs.”

Fueled by exponential data growth and expanding privacy regulations, data governance is a top priority for enterprises. Traditional approaches to data governance have long focused on restrictive data policies and procedures that hinder adoption and result in additional challenges for those responsible for the data. Effective data governance programs must focus on enabling users to leverage trusted, governed data to make informed, data-driven decisions. Organizations using an active, people-first approach to data governance, drive data culture and put quality data, not just any data, in everyone’s hands.

“A key part of ensuring data quality and reliable data pipelines is the ability to keep data teams and data consumers on the same page. With the integration between Alation and Bigeye, everyone — from data engineers all the way to business analysts — has a clear understanding of the health of the data,” said Egor Gryaznov, CTO and co-founder, Bigeye. “Pairing the leading data observability platform with Alation’s leading data catalog provides organizations with a holistic view of the quality of their data.”

"The modern data catalog is becoming a real-time, operational system that's deeply tied into an organization’s data ecosystem. For metadata, that means it is purposefully built into every step of the data value chain, as opposed to it being an afterthought. This allows data teams to truly see what's happening so that they can catch data issues before they reach consumers downstream," explains Maarten Masschelein, CEO and co- founder, Soda. "Through our partnership with Alation, users can discover and understand the data that matters to them, and trust they are working with data that is verified, reliable, and governed. Soda enables data producers and consumers to agree on what good data looks like by combining both machine learning-based alerting, as well as rules-based testing and validation of data. Every single operational metadata quality issue that Soda captures, can be made natively available in the Alation Data Catalog. We layer our data quality controls on top of Alation’s automated lineage to make it easy to do Root Cause Analysis (RCA), as well as assess the upstream and downstream impact of a data issue."

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