Be data-driven without

broken pipelines unknown data failures data drift schema changes anomalies broken dashboards distribution shifts broken ML-models outliers stale data volume shifts broken pipelines

Batch or streaming pipelines, get ahead of data failures with the next generation data quality platform for modern data teams.

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Get started with next generation data quality today

Validio is the only data quality validation and monitoring platform that scales with modern cloud-first organizations as they become increasingly data-driven.

The only platform for data at rest in data lakes & warehouses and data in motion in streams

Validio is the only data quality platform built to directly eliminate bad data through monitoring, validation and filtering of data in real-time streams and batches.

Abstracting complexity away from data engineering, enabling data-driven market leaders to trust the data they use to make decisions and build products.

Machine learning-based

Proven ML algorithms to automatically detect data failures on a datapoint level.


Statistical test-based

Robust and proven statistical tests and methods to detect data failures on a dataset level.


Rule-based

Supporting hard-coded rules to leverage human domain knowledge for detecting data failures.


Built with high throughput and performance in mind

All great products start with great architecture. As most new data quality tools focus on monitoring data at rest via SQL queries, Validio is the only platform that validates and monitors data at both datapoint and pipeline metadata level - coupled with real-time auto-resolutions for a proactive approach to data quality issues and unknown data failures.

Selected product features

Data in motion and at rest

Analyze both real-time streams and batch data depending on your data pipeline setup
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Statistical and ML-based

Utilize advanced statistical tests and machine learning algorithms
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Real-time

Batch or streaming pipelines, tests are performed in real-time, enabling a proactive approach to data quality
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High cardinality management

Built ground-up with high-cardinality in mind through hands-on experience
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Real-time auto-resolutions

Operate on data in real-time, rectifying bad data before negative downstream impact
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Multivariate analysis

For detecting more complex data quality issues that are multivariate in nature
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Infrastructure as code

Besides an intuitive GUI Validio also supports infrastructure as code
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Data partitioning

For more relevant and meaningful data quality monitoring and validation
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Dynamic autothreshold setting

Machine learning algorithms detecting patterns in datasets dynamically
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Customizable alerts

Send alerts to relevant stakeholders e.g. via Slack, email and Pagerduty
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Don’t just monitor pipeline metadata, monitor the actual data too. Don’t just alert upon bad data, resolve it as well.

Validio is the only data quality platform monitoring and validating batch and streaming pipelines in real-time on datapoint and dataset level, in addition to monitoring pipeline metadata. Built for the age of big data and the modern data stack.

Get started with state-of-the-art data quality validation and monitoring in minutes with Validio

Monitor your data before, during and after storage with Validio

If bad data is in your warehouse, it can often already be too late. The vast majority of new data quality tools focus solely on monitoring data quality on data that has already been stored in warehouses. Consequently, they cannot identify issues that occur before, during or after the data has been stored in e.g. Snowflake, BigQuery or Redshift. Expanding your data quality validation and monitoring to the left and right of the warehouse will buy you valuable time to catch and get ahead of data quality issues.

With Validio, you can monitor and validate data end-to-end in your data pipelines beyond simple SQL queries. Enabling a proactive approach to data failures that occur in your data pipeline before, during or after storing.

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Integrates seamlessly with modern cloud infrastructure

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Missing an integration?

We add new integrations continuously. If you don't see a technology in our integrations, contact us. We might already work on it or we can prioritize it.

More data isn't the magical asset organizations often think it is.

Good data trumps more data in almost every single case. Want to assess a company's data maturity? Ask how they evaluate the quality of their data, rather than how much data they have.

Patrik Liu Tran CEO & Co-Founder @ Validio / Co-Founder @ Stockholm AI
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Data pipelines have become the nervous system of the modern company and managing data quality is the beating heart

“Trust in data is essential. If people suspect the quality is faulty, that will likely translate downstream to lack of trust in the models and analytics the data produces.”

Sudhir Tonse Director of Data Engineering @ Doordash

“If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team.”

Andrew Ng Founder & CEO @ Landing AI / Adjunct Professor @ Stanford University

“Data quality and anomaly detection should be some of the first things we think about when we design data pipelines and we consume data. Not an afterthought.”

Laura Pruitt Director of Streaming Data Science & Engineering @ Netflix

“It doesn’t matter how advanced your data infrastructure is if you can’t trust your data.”

Eli Collins VP of Product @ Google

"Modern companies and institutions rely on data to guide every single business process and decision. Missing or incorrect information seriously compromises any decision process downstream."

Dustin Lange ML Science Manager @ Amazon

"Many organizations process big data for important business operations and decisions. As a metric of success, quantity of data is not enough - data quality must also be prioritized."

Arun Swami Principal Staff Software Engineer @ Linkedin

"In early 2019, the company made an unprecedented commitment to data quality and formed a comprehensive plan to address the organizational and technical challenges we were facing around data. We knew we had to do something radically different, so we established the data quality initiative."

Jonathan Parks Chief Data Architect @ AirBnB

"Without data quality guarantees, downstream service computation or machine learning model performance quickly degrade, which requires a lot of laborious manual efforts to investigate and backfill poor data."

Ying Zou Engineering Manager @ Uber

“Trust in data is essential. If people suspect the quality is faulty, that will likely translate downstream to lack of trust in the models and analytics the data produces.”

Sudhir Tonse Director of Data Engineering @ Doordash

“If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team.”

Andrew Ng Founder & CEO @ Landing AI / Adjunct Professor @ Stanford University

“Data quality and anomaly detection should be some of the first things we think about when we design data pipelines and we consume data. Not an afterthought.”

Laura Pruitt Director of Streaming Data Science & Engineering @ Netflix

“It doesn’t matter how advanced your data infrastructure is if you can’t trust your data.”

Eli Collins VP of Product @ Google

"Modern companies and institutions rely on data to guide every single business process and decision. Missing or incorrect information seriously compromises any decision process downstream."

Dustin Lange ML Science Manager @ Amazon

"Many organizations process big data for important business operations and decisions. As a metric of success, quantity of data is not enough - data quality must also be prioritized."

Arun Swami Principal Staff Software Engineer @ Linkedin

"In early 2019, the company made an unprecedented commitment to data quality and formed a comprehensive plan to address the organizational and technical challenges we were facing around data. We knew we had to do something radically different, so we established the data quality initiative."

Jonathan Parks Chief Data Architect @ AirBnB

"Without data quality guarantees, downstream service computation or machine learning model performance quickly degrade, which requires a lot of laborious manual efforts to investigate and backfill poor data."

Ying Zou Engineering Manager @ Uber

“Trust in data is essential. If people suspect the quality is faulty, that will likely translate downstream to lack of trust in the models and analytics the data produces.”

Sudhir Tonse Director of Data Engineering @ Doordash

“If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team.”

Andrew Ng Founder & CEO @ Landing AI / Adjunct Professor @ Stanford University

“Data quality and anomaly detection should be some of the first things we think about when we design data pipelines and we consume data. Not an afterthought.”

Laura Pruitt Director of Streaming Data Science & Engineering @ Netflix

“It doesn’t matter how advanced your data infrastructure is if you can’t trust your data.”

Eli Collins VP of Product @ Google

"Modern companies and institutions rely on data to guide every single business process and decision. Missing or incorrect information seriously compromises any decision process downstream."

Dustin Lange ML Science Manager @ Amazon

"Many organizations process big data for important business operations and decisions. As a metric of success, quantity of data is not enough - data quality must also be prioritized."

Arun Swami Principal Staff Software Engineer @ Linkedin

"In early 2019, the company made an unprecedented commitment to data quality and formed a comprehensive plan to address the organizational and technical challenges we were facing around data. We knew we had to do something radically different, so we established the data quality initiative."

Jonathan Parks Chief Data Architect @ AirBnB

"Without data quality guarantees, downstream service computation or machine learning model performance quickly degrade, which requires a lot of laborious manual efforts to investigate and backfill poor data."

Ying Zou Engineering Manager @ Uber

“Trust in data is essential. If people suspect the quality is faulty, that will likely translate downstream to lack of trust in the models and analytics the data produces.”

Sudhir Tonse Director of Data Engineering @ Doordash

“If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team.”

Andrew Ng Founder & CEO @ Landing AI / Adjunct Professor @ Stanford University

“Data quality and anomaly detection should be some of the first things we think about when we design data pipelines and we consume data. Not an afterthought.”

Laura Pruitt Director of Streaming Data Science & Engineering @ Netflix

“It doesn’t matter how advanced your data infrastructure is if you can’t trust your data.”

Eli Collins VP of Product @ Google

"Modern companies and institutions rely on data to guide every single business process and decision. Missing or incorrect information seriously compromises any decision process downstream."

Dustin Lange ML Science Manager @ Amazon

"Many organizations process big data for important business operations and decisions. As a metric of success, quantity of data is not enough - data quality must also be prioritized."

Arun Swami Principal Staff Software Engineer @ Linkedin

"In early 2019, the company made an unprecedented commitment to data quality and formed a comprehensive plan to address the organizational and technical challenges we were facing around data. We knew we had to do something radically different, so we established the data quality initiative."

Jonathan Parks Chief Data Architect @ AirBnB

"Without data quality guarantees, downstream service computation or machine learning model performance quickly degrade, which requires a lot of laborious manual efforts to investigate and backfill poor data."

Ying Zou Engineering Manager @ Uber

“Trust in data is essential. If people suspect the quality is faulty, that will likely translate downstream to lack of trust in the models and analytics the data produces.”

Sudhir Tonse Director of Data Engineering @ Doordash

“If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team.”

Andrew Ng Founder & CEO @ Landing AI / Adjunct Professor @ Stanford University

“Data quality and anomaly detection should be some of the first things we think about when we design data pipelines and we consume data. Not an afterthought.”

Laura Pruitt Director of Streaming Data Science & Engineering @ Netflix

“It doesn’t matter how advanced your data infrastructure is if you can’t trust your data.”

Eli Collins VP of Product @ Google

"Modern companies and institutions rely on data to guide every single business process and decision. Missing or incorrect information seriously compromises any decision process downstream."

Dustin Lange ML Science Manager @ Amazon

"Many organizations process big data for important business operations and decisions. As a metric of success, quantity of data is not enough - data quality must also be prioritized."

Arun Swami Principal Staff Software Engineer @ Linkedin

"In early 2019, the company made an unprecedented commitment to data quality and formed a comprehensive plan to address the organizational and technical challenges we were facing around data. We knew we had to do something radically different, so we established the data quality initiative."

Jonathan Parks Chief Data Architect @ AirBnB

"Without data quality guarantees, downstream service computation or machine learning model performance quickly degrade, which requires a lot of laborious manual efforts to investigate and backfill poor data."

Ying Zou Engineering Manager @ Uber

“Trust in data is essential. If people suspect the quality is faulty, that will likely translate downstream to lack of trust in the models and analytics the data produces.”

Sudhir Tonse Director of Data Engineering @ Doordash

“If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team.”

Andrew Ng Founder & CEO @ Landing AI / Adjunct Professor @ Stanford University

“Data quality and anomaly detection should be some of the first things we think about when we design data pipelines and we consume data. Not an afterthought.”

Laura Pruitt Director of Streaming Data Science & Engineering @ Netflix

“It doesn’t matter how advanced your data infrastructure is if you can’t trust your data.”

Eli Collins VP of Product @ Google

"Modern companies and institutions rely on data to guide every single business process and decision. Missing or incorrect information seriously compromises any decision process downstream."

Dustin Lange ML Science Manager @ Amazon

"Many organizations process big data for important business operations and decisions. As a metric of success, quantity of data is not enough - data quality must also be prioritized."

Arun Swami Principal Staff Software Engineer @ Linkedin

"In early 2019, the company made an unprecedented commitment to data quality and formed a comprehensive plan to address the organizational and technical challenges we were facing around data. We knew we had to do something radically different, so we established the data quality initiative."

Jonathan Parks Chief Data Architect @ AirBnB

"Without data quality guarantees, downstream service computation or machine learning model performance quickly degrade, which requires a lot of laborious manual efforts to investigate and backfill poor data."

Ying Zou Engineering Manager @ Uber

“Trust in data is essential. If people suspect the quality is faulty, that will likely translate downstream to lack of trust in the models and analytics the data produces.”

Sudhir Tonse Director of Data Engineering @ Doordash

“If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team.”

Andrew Ng Founder & CEO @ Landing AI / Adjunct Professor @ Stanford University

“Data quality and anomaly detection should be some of the first things we think about when we design data pipelines and we consume data. Not an afterthought.”

Laura Pruitt Director of Streaming Data Science & Engineering @ Netflix

“It doesn’t matter how advanced your data infrastructure is if you can’t trust your data.”

Eli Collins VP of Product @ Google

"Modern companies and institutions rely on data to guide every single business process and decision. Missing or incorrect information seriously compromises any decision process downstream."

Dustin Lange ML Science Manager @ Amazon

"Many organizations process big data for important business operations and decisions. As a metric of success, quantity of data is not enough - data quality must also be prioritized."

Arun Swami Principal Staff Software Engineer @ Linkedin

"In early 2019, the company made an unprecedented commitment to data quality and formed a comprehensive plan to address the organizational and technical challenges we were facing around data. We knew we had to do something radically different, so we established the data quality initiative."

Jonathan Parks Chief Data Architect @ AirBnB

"Without data quality guarantees, downstream service computation or machine learning model performance quickly degrade, which requires a lot of laborious manual efforts to investigate and backfill poor data."

Ying Zou Engineering Manager @ Uber

“Trust in data is essential. If people suspect the quality is faulty, that will likely translate downstream to lack of trust in the models and analytics the data produces.”

Sudhir Tonse Director of Data Engineering @ Doordash

“If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team.”

Andrew Ng Founder & CEO @ Landing AI / Adjunct Professor @ Stanford University

“Data quality and anomaly detection should be some of the first things we think about when we design data pipelines and we consume data. Not an afterthought.”

Laura Pruitt Director of Streaming Data Science & Engineering @ Netflix

“It doesn’t matter how advanced your data infrastructure is if you can’t trust your data.”

Eli Collins VP of Product @ Google

"Modern companies and institutions rely on data to guide every single business process and decision. Missing or incorrect information seriously compromises any decision process downstream."

Dustin Lange ML Science Manager @ Amazon

"Many organizations process big data for important business operations and decisions. As a metric of success, quantity of data is not enough - data quality must also be prioritized."

Arun Swami Principal Staff Software Engineer @ Linkedin

"In early 2019, the company made an unprecedented commitment to data quality and formed a comprehensive plan to address the organizational and technical challenges we were facing around data. We knew we had to do something radically different, so we established the data quality initiative."

Jonathan Parks Chief Data Architect @ AirBnB

"Without data quality guarantees, downstream service computation or machine learning model performance quickly degrade, which requires a lot of laborious manual efforts to investigate and backfill poor data."

Ying Zou Engineering Manager @ Uber

Download our latest whitepaper

The advent of big data and modern cloud data infrastructure has fundamentally changed the way organizations work with data. It’s time for data quality solutions to catch up with this new reality.

Download our latest whitepaper "Data quality in the era of Big Data and the Modern Data Stack" to read about how data infrastructure has changed during the past decade and the requirements for a future-proof data quality solution.

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Validio is used by leading data-driven organizations

From startups to multi-billion dollar unicorns, Validio is used by data leaders of all sizes. Reliable data pipelines are as important for the success of analytics, data science, and machine learning as reliable supply lines are for winning a war. We believe that you shouldn’t have to be an AirBnB, Uber or Netflix in order to have advanced ML-based data quality technology in place. We also believe that modern data teams and data engineers get better ROI by spending their time on other business-critical tasks rather than building and maintaining their own data quality infrastructure.

Request a demo and learn how fast you can get started with state-of-the-art data quality validation and monitoring. We place a special emphasis on being a non-nonsense data quality partner focusing on time-to-value.

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