Schema drift detection and real-time quality checks at the edge. Every payload screened. Every decision returned in under 10ms.
Pipelines don't fail loudly. They succeed with corrupt, drifted, or incomplete data. By the time a dashboard breaks, the damage is done.
Upstream services add, remove, or rename fields silently. Your ingestion layer has no idea until something breaks downstream.
A field drops from 99% to 40% completeness between batches. No alert fires. Thousands of bad rows enter your warehouse unchecked.
A numeric field becomes a string. ML features break. Aggregations silently return wrong results. Downstream effects cascade for hours.
By the time a dashboard shows NaN, the cause was at ingestion four hours ago. The cost is time, trust, and a war-room call.
One endpoint between your sources and your storage. No dashboards. No manual rules. No delays.
One POST per payload. No SDK required. Works with any language, framework, or data format. Integrate in under 30 minutes.
Schema fingerprinting, null rates, type stability, percentile distribution — all computed in-memory at the edge in under 10ms.
A structured JSON verdict with health score, issue breakdown, and drift flags. Your pipeline acts on it. Bad data never reaches storage.
Use the public test key below. Edit the JSON, hit Send, and see a real quality decision returned instantly.
Hit Send to screen your payload
18 quality signals computed in real-time at the edge. No rules to write. No thresholds to configure. Works from the first request.
Fields added, removed, or renamed between batches. Catches upstream API changes before they silently break your models.
A numeric field suddenly contains strings. A boolean becomes an integer. Caught per column, per batch — before your warehouse casts it wrong.
Null rates and empty strings tracked against baselines. When a field drops from 99% complete to 40%, you know instantly — not four hours later.
Values outside expected ranges flagged automatically. Revenue of $-999, ages of 350, timestamps from the future — all caught.
Row count deviations, duplicate records, and unexpected cardinality changes. Batch that normally has 10K rows shows up with 47? Blocked.
All signals combined into a single health score and a clear decision — PASS, WARN, or BLOCK. Globally, at the edge, without touching your storage.
No SDK. No configuration. Drop one request into your existing pipeline and you're live.
Computed at the edge. Adds negligible latency to your pipeline. Not a bottleneck.
One HTTP POST. Works with any language, stack, or data format you already use.
Raw payloads are discarded immediately. Only aggregated quality metrics are stored. Full details in our privacy architecture.
Every quality check uses a versioned, auditable sampling strategy. Returned in every response for full traceability.
One API call wraps any ingest point. Bad payloads get blocked before they corrupt a single row. No custom validation logic required.
Schema drift and type mismatches caught at the source. Spend time building models — not debugging why last night's job failed silently.
Every metric you trust starts with data verified at ingestion. No more NaN totals, no more war-room calls, no more bad numbers in exec reports.
One bad data incident costs more than a year of this plan.Pay for what you screen. Scale linearly. Cancel anytime. Questions? Email.app@datascreeniq.com
Your payload is processed entirely in-memory inside a Cloudflare Worker. Nothing is ever written to disk or logged.
Processing happens in Cloudflare's global edge network. No central server ever receives your raw data.
We store schema fingerprints, null rates, and type statistics. Non-reversible. No row-level data, no PII, ever.
API requests are not logged at the edge layer. Only billing counters (request count, not content) are tracked per customer.
No setup. No configuration. Send your first request and get a quality report back immediately.
Get your free API key