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Eliminate

bad data broken pipelines unknown data failures anomalies broken dashboards broken ML-models outliers bad data

Stop firefighting bad data with the
next generation data quality platform

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Get started with next generation
data quality today

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

Trust your data in warehouses, data lakes and streams

Get complete trust in your data for any use case, whether it's classical BI or more advanced machine learning and operational real-time analytics where data might never touch the warehouse.

Save time with smart alerts and auto-thresholds

Choose between rule-based or auto-threshold monitors in an Intuitive UI that adapt to trends and seasonality over time.

Overall, this enables you to spend less time setting up and maintaining data quality over time.

Create better data pipelines by writing out bad data in real-time

Validio enables bad data to be written to a data destination of your choice—effectively filtering it out. This enables data to be fully operationalized. Even if some percentage of bad data is expected, the pipelines won't break.

If major bugs appear, bad data can be manually inspected in a data visualization tool of your choice—leading to faster resolution.

Spend more time building robust and scalable systems instead of firefighting bad data.

Powerful partitioning

Averages are dangerous and can often hide the truth. With partitioning, you can compare apples to apples by looking at anomalies in individual sub-segments of the data.


Univariate and multivariate

Set up validation on single dimensions, as well as on dependencies between dimensions. Because let’s be honest—real data has dependencies in it.


Metadata and actual data

Validate your data from a bird’s eye view (like freshness and schema changes) as well as the nitty gritty details (like each individual data point meeting domain-specific rules.


Comprehensive data quality validation and monitoring

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, fixing bad data before it downstream consumption
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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

Compare apples to apples by validating individual data segments
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Dynamic autothreshold monitors

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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State-of-the-art data quality in minutes

Trust the data you use to make decisions & build products in both batch and streaming pipelines

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 validating pipelines in real-time on datapoint, dataset and metadata level, enabling you to write out bad data to a data destination of your choice.

Validio integrates seamlessly with your data pipelines so you can get complete trust in your data, knowing you will catch any data quality failures before downstream data consumers do

Integrates seamlessly with modern cloud infrastructure

Google Big Query

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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Recent articles

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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