#data validation testing tools

Datametica’s blog underscores the importance of cell‑level data validation in today’s data‑driven world. It explains that ensuring data accuracy and integrity is essential to avoid costly errors and maintain trust. The article highlights how cell‑level validation preserves data integrity, prevents errors, enhances quality, meets regulatory compliance, and supports better decision‑making. It notes that traditional data validation testing tools are often manual and iterative, making them impractical for complex modern data pipelines.

When disaster strikes, restoring your data isn’t enough — you need to validate that every piece is accurate, complete, and production-ready.

In our latest blog, Datametica explores how automation is transforming disaster recovery, with tools that verify data integrity without manual intervention.

At the heart of our approach is Pelican, an AI-powered automated data validation tool that compares, reconciles, and certifies datasets at scale — across environments like production, DR, and backup.

Unlike traditional data validation testing tools, Pelican works with APIs, has built-in scheduling, and performs intelligent checks down to the cell level — without needing to move or recode your data.

Cisco Enhances Security and Efficiency with Automated Data Validation Tools

Cisco successfully leveraged Pelican, a cutting-edge data validation tool, to enhance security and efficiency in its internal migration initiative. By implementing an automated data validation tool, Cisco eliminated manual validation, ensuring 100% secure data validation without data movement. This transformation resulted in zero test failure rate, validation of over 2 trillion rows, and organization-wide adoption, empowering 150+ data engineers. The collaboration between Cisco and Onix showcased the power of scalable data validation solutions, setting a new benchmark for data integrity and validation testing in large enterprises.

iCEDQ for ETL Testing -  Big Data Lakes Data Warehouses

People are rapidly adopting cloud architectures for Data Warehouses and Machine Learning projects due to the economies of scale in the cloud. One obstacle in achieving rapid success is the data and data types inconsistencies between on-premise structures and the modern data stacks in the cloud.