
DataScreenIQ
Real-time data quality screening for data pipelines — PASS /
Details
- Follow on
- Categories
- Developer ToolsData & Infrastructure
- Use Cases
- Data AnalysisData Integration
- Target Audience
- DevelopersDevOps EngineersData Scientists
- Pricing
- Freemium from $19
- Platforms
- API
- Alternative To
Soda
Monte Carlo
About DataScreenIQ
DataScreenIQ — Real-time data quality firewall for data pipelines. Screens every payload at the ingest boundary before your warehouse sees a single row. Returns PASS / WARN / BLOCK in under 10ms. Detects: Schema drift — fields added, removed, or renamed Type mismatches — numeric field now has strings Null rate spikes — completeness drops overnight Distribution anomalies — values outside expected range Duplicate rates — unexpected cardinality collapse Timestamp staleness — data older than expected Empty string rates — "" masquerading as null Row count anomalies — batch size deviates 3× Integrates with: Apache Airflow · Prefect · dbt · GitHub Actions · any HTTP client Free tier: 500K rows/month · Runs on Cloudflare Workers · Zero data retention https://github.com/AppDevIQ/datascreeniq-python
Product Insights
DataScreenIQ provides an API-first data quality firewall that performs real-time payload screening at the ingest boundary for data pipelines. It utilizes a freemium pricing model and integrates with orchestration tools like Apache Airflow and dbt.
- Sub-10ms response times for PASS, WARN, or BLOCK results.
- Detection of schema drift, type mismatches, and distribution anomalies.
- Zero data retention policy ensuring privacy for ingested payloads.
- Cloudflare Workers infrastructure for global low-latency performance.
Ideal for: Developers, DevOps Engineers, and Data Scientists who need to validate data quality and schema integrity before information enters their warehouse.
DataScreenIQ is positioned as an alternative to data observability and quality tools such as Soda, Monte Carlo, and dbt.
Screenshots
Reviews (2)
Average 4.5 out of 5
Based on 2 reviews






Comments (1)
I built DataScreenIQ because every data engineer (including me) has been burned by silent data failures , it solves a long standing issue , that has been a issue for data engineers and it solves it