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Product Launch: has officially leveled up its integration with Snowflake’s new data quality capabilities

PRODUCT LAUNCH: enables trusted conversations with your company’s data and knowledge with the AI Context Engine™


Accelerate adoption of AI with the AI Context Engine™️, now generally available

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Streamline DataOps Workflows

DataOps applies agile software development best practices to data management, making data work reusable and reproducible. As the backbone of your data operations strategy, provides a single place for data producers and consumers to communicate and share important data trust signals and metadata on increasingly complex data pipelines and data-driven applications.

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Save Engineering Time

Reduce the time spent on manual DataOps tasks

When organizations depend on data for their day-to-day operations, it’s critical to communicate data pipeline issues, outages, and improvements to relevant data teams and data users. By automating and streamlining updates from DataOps teams to the rest of the organization, data engineers can ensure that dashboard and report updates are timely and relevant while saving time and resources for higher-impact projects. 

Drive Business Decisions

Empower data users to scale insights

Provide data users with data context and trust signals to increase confidence in the data they’re using for data analytics and data-driven decision-making. With’s DataOps application, context like business terms, metrics, lineage, and more is available to data users in the data analytics tools they work with, like Tableau and PowerBI. With this context, data consumers can explore and discover more about their data to find insights.

Spot Pipeline Issues

Get end-to-end visibility for your data supply chain

Investigating data pipeline issues and identifying all relevant upstream and downstream dependencies is time-consuming. Column-level lineage across your modern data stack helps you pinpoint, understand, and resolve issues faster.

Operationalize data trust and collaboration

A diverse group of organizations – from an array of industries, organization sizes, and business models – uses to ground their DataOps, data management, and data quality processes and communication. Teams use to deliver seamless business continuity during production outages and reduce time spent by data teams on manual, repetitive communication and cataloging tasks by using AI-powered automation.

Increase trust in data products

With, Penguin Random House UK’s team created a single source of truth where data consumers can discover accurate, trusted data products. Today, all of their approved data products are found in with data owners and more than 1,600 measures. Data scientists, who used to take a week to find trusted data, could now find it in under a minute.

Optimize data engineering workflows

Vopak’s team came to looking to improve internal processes, streamline data operations, and unveil relevant business insights. made it easy for their data engineering team to manage and connect many data sources while enabling data access to business-critical data for data modelers and analysts.

Making data and context easier to self-serve

OneWeb’s data teams needed a self service approach to support their data mesh strategy across 30+ data lakes and enable their data consumers to find trustworthy data products. provides business value to OneWeb’s data hub with semantic search for better discoverability and data lineage to help data consumers understand data flows, trust data pipelines, and increase confidence in their data.


Schedule a Demo

Schedule a demo with the team to see how our product capabilities can help your data teams streamline data workflows and improve trust and data governance.

“Data professionals spend 56% of their time on operational execution and only 22% on innovation that delivers value”
Gartner Melody Chien, Nick Heudecker, “Survey Analysis: Data Management Struggles to Balance Innovation and Control,” March 19, 2020

Fully integrated — All your metadata in one place.

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"97% of data engineers are feeling burnt out"

Read why burned out data engineers are calling for DataOps in the whitepaper from and DataKitchen

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Automate DataOps workflows

Eliminate tedious, manual tasks by automating data pipeline updates, stakeholder communication, and workflows. Provide real-time updates to data consumers to ensure that they always know the freshness status of data feeding their reports and dashboards.

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Increase data trust via everyday tools

Grow your organization’s data culture and increase confidence and trust in data across the organization. Surface visible trust signals and contextual insights directly in BI, communications, and collaboration tools that data consumers use to enhance data analysis and self service.

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Enhance your impact analysis

Accelerate your root cause analysis and issue resolution for data pipelines by using column-level lineage to understand data flow along with upstream and downstream dependencies. Utilize data on dashboard and report usage, monitor status history, and more to prioritize and optimize projects.

Data teams spend ~30% of their time answering the same user questions again and again.

See how your DataOps team can close the communication gap with your data users

Data engineering teams spend as much as 30% of their time answering (and re-answering) questions about the freshness and quality of data and reports –  pulling them away from other critical work. To learn more about using data monitors and trust signals to keep your teams in the loop about your data assets' freshness and data lifecycle status, watch this digital event on demand.

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