Batch or streaming pipelines, get ahead of data failures in real-time with the fastest data reliability platform for modern data teams.
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The Validio platform is 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.
Proven ML algorithms to automatically detect data failures on a datapoint level.
Robust and proven statistical tests and methods to detect data failures on a dataset level.
Supporting hard-coded rules to leverage human domain knowledge for detecting data failures.
As most new data quality tools focus on monitoring data at rest via SQL queries, Validio has the ability to validate and monitor streaming and batch data pipelines instantaneously in real-time enabling proactive identification and mitigation of data quality issues and unknown data failures.
Validio has been built to be able to provide real-time auto-resolutions to bad data, apply rich multivariate data quality analyses and partition the data before analyzing.
Validio is for modern data teams looking for a data quality platform that they can grow and scale with as they become increasingly data-driven.
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.
Validio can be used for analyzing both data in motion (streaming data) and data at rest (batch data). This enables proactive data quality management, as mitigations can be undertaken at the source of an error as soon as it occurs.
When data failures are identified, alerts are sent to relevant stakeholders (through e.g. Slack, email, PagerDuty), enabling them to take timely and corrective action. Alerts can also e.g. trigger retraining of machine learning models in production in response to e.g. data drift.
Validio automatically monitors for data failures using statistical tests and machine learning algorithms, while also supporting hard coded rules to transfer specific human domain knowledge. To get up and running requires minimal time investment.
Validio is deployed in the customers’ cloud environment (meaning that no data is sent to external environments) with an entirely automated and remotely managed in-VPC/Cloud-prem deployment. Validio also offers a fully managed SaaS version with general availability to be announced later. We understand the importance of deployment optionality, especially for sensitive data use cases.
Tests are performed in real-time, enabling a proactive approach to data quality management as users are notified about potential issues as soon as they emerge and can act on them before causing significant havoc in downstream applications
Validio is built ground-up with high-cardinality in mind. Applications now utilize thousands of data tables with hundreds or thousands of columns each that can’t be inspected manually or with manually defined data quality rules.
Make Validio a part of your pipeline operating on data in real-time, rectifying bad data before it enters the main data pipeline and affects data consumers and data products. Use automated real-time data filters and imputations to keep downstream applications safe from data failures.
Validio uses machine learning algorithms to detect patterns in the datasets. This way, thresholds are automatically defined, flagging unusual data points. As data changes, the alert thresholds are automatically updated based on data quality metrics forecast.
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 instantly as it arrives or leaves your data warehouse, enabling a proactive approach to data failures that occur in your data pipeline before, during or after storing.
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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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.
“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
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.
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.
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