Serverless, Autonomous Data Validation in Snowflake

Ensure Superior Snowflake Data Quality With the Help of Data Trust Score

Data Quality Validation for Snowflake

Data Quality and trust are keys to making the most efficient use of data. DataBuck enables Snowflake users to evaluate Data Quality with a trust score for all data assets. DataBuck autonomously detects Data Quality specific to each dataset’s context and saves 95% of the time spent on discovering, exploring, and writing data validation rules.

First Eigen

Truly-Serverless

People Productivity

Autonomous DQ

First Eigen

In-situ (Powered by Snowflake)

Transition from a manual model to an automated trust-based approach

First Eigen

DataBuck calculates an objective Data Trust Score for each data asset (Schema, Tables, Columns) using its ML capabilities. Trust in data will no longer be a popularity contest. No need to have individuals give their subjective opinion on the health of a table/file. All stakeholders can universally understand the objective Data Trust Score. More importantly, DataBuck will map and update the data trust score to the SNOWFLAKE “OBJECT TAG” and the relevant data assets without any human intervention or complex integration efforts.

DataBuck can auto-trigger Data Trust Score as soon as new data lands in a Snowflake table or can be scheduled to run at specific time.

DataBuck can transition you from a traditional manual model to a trust-based, data-driven approach to data quality.

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How It works

  • Scan: DataBuck scans each data asset in the Snowflake platform. Assets are rescanned every time the data asset is refreshed or whenever a scheduler invokes DataBuck. Scanning is done in-situ, i.e., no data is moved to DataBuck.
  • Auto Discover Metrics: DataBuck autonomously creates data health metrics specific for each data asset. The well-accepted and standardized DQ tests are customized for each data set individually, leveraging AI/ML algorithms.
  • Monitor: Health metrics are computed based on quality dimensions for each column in the data asset and monitored over time to detect unacceptable data risk. Health metrics are translated to a data trust score.
  • Alert: DataBuck continuously monitors the health metrics and trust score and alerts users when the trust score becomes unacceptable.
Data quality dimension level

The summary of results displays the deviation in the trust score. It shows how the health and quality changed between the last two analyses and how much the user can trust the data.

Every violation discovered can be double-clicked for further information:

  • Users can expand the dimension to see which columns are affected at the data asset level. Click a column name to see the dimension details for that column.
  • At the column level, click the dimension name for further details.

Users can then decide whether a specific Data Quality violation can be ignored or flagged for further analysis, either for the entire data asset or individual column.

What DataBuck users say…

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

- VP Technology, Enterprise Data Office, Major US bank

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

- CIO of US Financial Services firm

“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.”

- Head of Enterprise Data Quality Monitoring, Major US bank

“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.”

- VP, Enterprise Information Management, Information Governance Leader, Insurance Company

“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.”

- Sr. Exec. Technology Office, Top-3 African bank

“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.”

- Sr. VP Data Mgmt and Analytics, US Investment Bank

“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.”

- VP Technology, Enterprise Data Office, Telehealth provider

“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.”

- Director, Commercial Data Operations, US pharmaceutical

“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.”

- Director, IT – Data Strategy, Financial Planning, Fortune-50 Hi Tech manufacturer

Introduction DQ Monitoring on AWS
FirstEigen recognized in AWS re:Invent as best-of-breed DQ tool
Autonomous Data Quality validation on Cloud
How AI/ML simplifies Data Quality and increases accuracy

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