Autonomous Data Validation in AWS S3

“What took my team of 10 Engineers 2 years to do, DataBuck could complete it in <8 hrs”

“DataBuck’s Data Quality automation does 80% of the heavy lifting for us with just 5% of the effort.”

“Streamlining the DQ monitoring and validation process w/DataBuck has reduced our time-to-market. With fewer resource we auto discover DQ rules, which also self-heals as the data evolves.”

“DataBuck can really add a lot of headcount efficiency for us. This tool makes it easy for us to not only profile and discover the rules, but also to operationalize them and auto-heal as the data evolves over time.”

“AML is on the rise. We have data from 10 countries in different formats and standards that need to be validated. We could not keep up doing it manually. DataBuck has automated and streamlined our data pipeline.”

“In the last 3 years we’ve had a 100x increase of API’s and microservices on the Cloud. This proliferation is beyond what Data Stewards can manage. As Cloud-native tool designed for Data Engineers, DataBuck autonomously validates data upstream and tremendously eases the burden on Stewards.”

“Monitoring and validating files and data at ingestion directly impacts our revenues. DataBuck gives us the reliability, intelligence and speed we need to eliminate revenue-leakage.”

“Aggregating weekly sales data from many dozens of sources and validating them is laborious and error prone. With DataBuck’s AI/ML-driven DQ automation we got more accurate data with less than 10% effort.”

“With the traditional Data Quality tools, we could not thoroughly audit the financial data for the Street w/in our audit window. DataBuck’s performance has reduced data validation times from 11 hrs to 2 hrs, and w/higher accuracy.”