Automated Data Quality and Monitoring

Validate in lower environments & monitor in Production

Data serves as the foundation for any business. However, the presence of flawed data diminishes its utility. Utilizing inaccurate data involves relying on irregularities that could lead to chaos when making decisions. Enter Stardata, the comprehensive data testing and data quality monitoring platform designed for traditional data and big data. This tool automates data testing, ensuring the integrity of your data by validating its accuracy and fitness for purpose.

What Is Stardq?

Stardq is an automated data testing and data quality monitoring platform crafted to detect potential data issues within your data ecosystem having traditional data and big data. Due to its adeptness in pinpointing data issues, it serves as an automation solution for data testing, ETL Testing, Data Warehouse Testing, Data Migration Testing, Big Data Testing, and Production Data Monitoring.

With its distinctive in-memory, parallel & distributed engine, Stardq enables organizations to seamlessly implement end-to-end automation for Data Testing and Data Quality Monitoring.

How Stardq Works?

Connect: The engine connects to the configured data sources. Then it reads the data from source and target into the memory.

Expectations: Analyst or Tester defines the data expectations in simple english language which becomes input for data validations.

Validate: Based on the defined expectations, data gets validated to resurface the bad data having anomalies.

Report: The system captures data validation results and the report & dashboard is available for users to analyze the data issues and take actions based on the results.

Features

Automted Data Testing, Quality and Monitoring at scale

Collaboration

The effort of data testing, quality and monitoring spans across different teams in any organization. Users can create/define, reuse expectations across team, projects, release quickly. Developers can do unit testing, Testing/QA team does integration testing, Analysts do UAT and business users monitor data in production. This collaboration is possible with Stardq as it has a centralized database repository and web-based graphical user interface dashboard.

In-Memory Engine

The core feature of Stardq is its engine used to validate the full volume of the data in-memory. This allows Stardq to go across data sources and validate the data efficiently. Customers can run their data testing, quality and monitoring processes on in-memory, parallel, distributed and horizontally scalable engine based on their requirement.

Reporting

Stardq captures all the metadata about the expectations and their results in a database repository. This enables users to create and view data testing and data quality reports and dashboard using our web based reporting tool.

Standard edition

This edition is used for traditional data testing and It is the most deployed edition of Stardata. It uses in-memory, parallel and distributed engine for validating data against expectations.

Big Data Edition

This edition is used for validating big data against expectations which uses in-memory, parallel & distributed engine. You can scale the performance based on the size of the system.

Big Data Edition

This is our big data edition engine which uses Apache Spark cluster to do all the processing. You can scale the performance based on the size of your cluster.

Charlie Baptista

“At our bank, we rely heavily on automated data testing capabilities of stardq to resurface bad data and remediate it.”

The platform uses defined expectations to validate data and capture validation results in metrics repository.

Adam Smith

“We use automated data testing and quality capabilities of stardq to resurface data anomalies and enhance data quality.”

The platform provides data quality and validation reports and dashboard to understand data quality issues and remediate it.