DataScreenIQ provides real-time data quality screening at the ingestion boundary, acting as a data firewall to prevent bad data from entering data pipelines. It is designed to catch issues such as schema drift, type mismatches, spikes in null values, distribution anomalies, and unexpected duplicates before data reaches storage or downstream systems. The tool targets scenarios where upstream data changes—such as silent API field modifications or sudden shifts in data completeness—can introduce errors that are only detected after the fact, causing broken dashboards and time-consuming debugging.
The service operates as an API endpoint that can be integrated into data pipelines with a single POST request per payload. It is compatible with any language, framework, or data format, and offers a native Python SDK for integration. DataScreenIQ performs in-memory, edge-native statistical checks on every payload, including schema fingerprinting, monitoring of null rates, type stability, and percentile distribution analysis. For each batch of data, it returns a structured JSON verdict—PASS, WARN, or BLOCK—along with a health score, issue breakdown, and drift flags, all within milliseconds. This enables pipelines to automatically act on screening results and prevent problematic data from entering storage.
Additional features include webhook and Slack alerting on BLOCK decisions, so teams are notified instantly when a data source is blocked due to quality issues. The platform also provides baseline management, allowing users to reset baselines when schema changes are intentional, ensuring the system adapts to evolving data structures without persistent false positives. A full dashboard and per-source configuration are built in for production use cases.
DataScreenIQ offers a free tier that allows up to 500,000 rows per month without requiring a credit card. The service is accessed via its API, with public test keys available for immediate trial and live pipeline simulation. By providing a real-time, pre-ingestion quality layer, DataScreenIQ aims to address the limitations of traditional post-ingestion data quality tools and reduce the operational burden of managing silent data failures.
datascreeniq sits in PulseGate's API design, testing & docs category. It focuses on ensuring data quality and integrity in real-time data pipelines and ETL processes. It is built as an open-source project for data engineers. datascreeniq is open source under the MIT license. The product ships for the web, the command line, and API.
AppDevIQ builds and maintains datascreeniq, and the product first shipped in 2026. Development happens publicly on GitHub with 42 commits in the last 90 days. PulseGate's similarity index finds few close equivalents — datascreeniq occupies a relatively distinct niche. Key capabilities include data validation, real-time screening, and API integration. It exposes integrations via a public API.
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