The real-time data reliability platform.

An automated data reliability platform designed for operational use cases. Abstracting complexity away from data engineering.

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Data quality validation and monitoring for data in motion and data at rest

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

Don’t just monitor pipeline metadata, monitor the actual data too. Don’t just alert upon bad data, resolve it as well.

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Machine learning-based

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


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Statistical test-based

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


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Rule-based

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


Data quality for the age of big data and modern cloud data infrastructure.

Validio’s real-time data reliability platform is purpose-built for continuously monitoring and validating data in the age of modern cloud data infrastructure and big data with high cardinality of data & data sources and constantly changing properties.

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Selected product features

Data in motion and at rest

Analyze both real-time streams and batch data
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Customizable alerts

Send alerts to relevant stakeholders e.g. via Slack, email and Pagerduty
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Statistical and ML-based

Utilize advanced statistical tests and machine learning algorithms
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Non-intrusive

Deploy in your cloud environment, no data is sent to external environments
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Real-time

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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Dynamic autothreshold setting

Machine learning algorithms detecting patterns in datasets
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Monitor & validate your data before, during and after storage

It’s not sufficient to only monitor data at rest in the cloud data warehouse.

The vast majority of current data quality tools focus on monitoring (through SQL queries) data stored in warehouses (e.g. data at rest). Consequently, they cannot identify issues that occur before, during or after the data has been stored in e.g. Snowflake, BigQuery or Redshift.

The vast majority of data quality issues - especially unknown data failures - occur before, during or after data has been stored. Today, automated ELT pipelines are made up of several best-of-breed tools working together at different stages in the data pipeline. This results in new and unique forms of data failures that are harder to pinpoint and can propagate quickly, especially if they're not catched at the source.

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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.

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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 data quality 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

“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

“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

“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

“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

“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

“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

“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

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.

Download whitepaper
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Managing data quality shouldn’t be hard.

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.

Join the waitlist and get notified of our self-service release.

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