Authenticate Cloud Data Pipeline with Autonomous Data Trustability Validation

Scalable

Scalable

Set up 1,000 data assets in less than 40 hours

Fast

Time reduction to onboard data set

Validate 100 million records in 60 seconds

Better

Reduction in unexpected errors

Look for 14 types
of data errors

Economical

Cost reduction

Validate 10,000 Data Assets in less than $50

Secure

Increase in processing speed

No Data leaves your Data Platform

Integrable

Cloud native

Data Pipeline Data Governance Alert System Ticketing System

How does DataBuck help authenticate the Data Pipeline?

DataBuck is an autonomous Data Trustability validation solution, purpose-built for validating data in the pipeline.

  • 1,000’s of Data Trustability and Quality checks are auto-discovered and recommended.
  • Thresholds for those checks are auto-recommended by the Artificial Intelligence program.
  • Business users can adjust thresholds in a self-service dashboard, without IT involvement.
  • Data Trust Score is auto-calculated for every file and table.
  • The Data pipeline can be controlled by the Data Trust Score of the overall file or any individual Data Quality dimension.
  • Errors can be stopped from contaminating downstream data by robust data pipeline control.

DataBuck as Part of the Pipeline

A) Run DataBuck on ADF (Azure Data Factory), AWS Glue, Databricks, Talend, DBT, Fivetran, Matillion, Informatica or any ETL tool that supports rest API/Python

B) Integrate with enterprise scheduling system (e.g. Autosys)

C) Use the built-in scheduler

Benefit of automating Data Trustability and Quality validation with Machine Learning

Get drinkable, crystal clear stream of data from the pipeline along with these benefits…

People Productivity

People productivity
boost >80%

Reduction in unexpected errors

Reduction in unexpected errors: 70%

Cost reduction

Cost reduction >50%

Time reduction to onboard data set

Time reduction to onboard data set ~90%

Increase in processing speed

Increase in processing speed >10x

Cloud native

Cloud native

Our Popular Blogs

Pharmaceutical Industry

Data Quality Issues Affecting the Pharmaceutical Industry: Finding a Solution

By Seth Rao
Pharmaceutical enterprises worldwide navigate a complex ecosystem where vast amounts of sensitive datasets are central to their operations. The data typically includes information about clinical trials, electronic health records (EHR), ...
Read More →
Data Management

Agentic Data Trust: Next Frontier for Data Management

By Angsuman Dutta
As data grows exponentially, ensuring accuracy, security, and compliance is increasingly challenging. Traditional rule-based data quality checks—whether downstream (reactive) or upstream (proactive)—still produce substantial manual overhead for monitoring and resolving ...
Read More →
Data Trust

5 Emerging Data Trust Trends to Watch in 2025    

By Angsuman Dutta
As organizations accelerate their data-driven initiatives, data quality is evolving from a manual, back-office function to a core business priority. By 2025, we’ll see a new generation of data quality ...
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Read our White Papers

A Framework for AWS S3/Azure ADL/GCP Data Lake Validation

With the accelerating adoption of AWS S3/Azure/GCP as the data lake of choice, the need for autonomously validating data has become critical. While solutions like Deequ, Griffin, and Great Expectations provide the ability to validate AWS/Azure/GCP data, these solutions rely on rule-based approach that are rigid, non-flexible, static, and not scalable for 100’s of data assets and often prone to rules coverage issues.

Solution: A scalable solution that can deliver trusted data for tens of 1,000’s of datasets has no option but to leverage AI/ML to autonomously track data and flag data errors. It also makes it an organic, self-learning system that evolves with the data.

AWS S3/Azure ADL/GCP Data Lake Validation
A Framework for AWS S3/Azure ADL/GCP Data Lake Validation

13 Essential Data Validation Checks for Trustworthy Data in the Cloud and Lake

When data moves in and out of a Data Lake or a Cloud, the IT and the business users are faced with the same question- is the data trustworthy?

Automating these 13 essential data validation checks will immediately engender trust in the Cloud and Lake.

Download this white paper today!

First Eigen
13 Essential Data Validation Checks for Trustworthy Data

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

Friday Open House

Our development team will be available every Friday from 12:00 - 1:00 PM PT/3:00 - 4:00 PM ET. Drop by and say "Hi" to us! Click the button below for the Zoom Link:

Friday Open House - Talk to Us Live!