Authenticate Cloud Data Pipeline with Autonomous Data Trustability Validation

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


Building trust in data is janitorial work; it’s not sexy, but critical for basic operations

Data is the lifeblood of any organization, and ensuring that it is accurate and trustworthy is essential to success. Yet, data engineering is often seen as a dull and unglamorous ...
Read More
A digital city symbolizing data warehouse architecture.

Data Warehouse Architecture: Traditional vs. Cloud

A data warehouse is a system that gathers data from various sources and makes it available to management and others to support and improve their decision-making. Data warehouses can employ ...
Read More
Data observability platforms help companies measure the health of their data.

How to Choose a Data Observability Platform

Not only do submarines not have screen doors, but they don’t have windows or external lights at all. Submarines primarily use a combination of sonar and radar to navigate, but ...
Read More

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.

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!

What DataBuck users say…

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: