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Fix data problems at the source before your model ever sees them.

Data Drift Scan

Monitors the statistical health of data pipelines feeding production models, detecting schema changes, volume anomalies, and distribution shifts at the data layer before they corrupt model outputs. Catches upstream data quality issues (missing fields, encoding changes, sensor failures) before they propagate to model predictions.

Why It Matters

DataDriftScan moves the defense line upstream — catching data problems in the pipelines before they ever touch a model.
Schema changes, sensor failures, and encoding shifts get flagged at the data layer, turning what would become mysterious model degradation into a precisely located pipeline fix.
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Problems caught at the source, not the symptom

A silently renamed field or failed sensor corrupts predictions in ways that take weeks to trace backward — pipeline-level detection isolates the cause the moment it happens.

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Column-level precision for fast root cause

Drift statistics per column with historical trends point engineers to the exact field and the exact day it changed — root cause isolation in minutes, not forensic investigations.

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Protects every model downstream

One monitored pipeline typically feeds many models — fixing data health at the layer where problems originate protects the whole model portfolio at once.

The Cloudly Advantage

DataDriftScan completes Cloudly’s defense-in-depth story — DataQualityGuard gates training data, DriftShield watches model behavior, and DataDriftScan monitors the pipelines in between.
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Completes three-layer monitoring

Data pipelines, training data, and model behavior each covered — a defense-in-depth pitch that makes single-point monitoring competitors look incomplete.

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Attaches to every data engineering deal

Any pipeline we build with Airflow, Kafka, or dbt can ship with health monitoring included — raising contract value on engagements we're already winning.

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Kafka and Glue services pull-through

Pipeline monitoring deepens our Cluster Services work on Kafka and opens AWS Glue Data Quality conversations in every AWS account.

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Sharpens DriftShield's value

Distinguishing "data broke upstream" from "the world changed" makes both products smarter — bundled, they cut false retraining cycles that waste compute.

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Speaks to data engineers

A buyer persona our model-centric products don't reach — expanding the platform conversation to the teams who own the pipelines.

The Final Takeaway

24/7 pipeline monitoring revenue — Continuous data health monitoring converts directly into managed-service contracts — recurring revenue that scales with every pipeline the client adds.

Powered By

Apache Kafka monitoring

Core to the DataDriftScan technology stack.

AWS Glue Data Quality

Core to the DataDriftScan technology stack.

Great Expectations

Core to the DataDriftScan technology stack.

dbt tests

Core to the DataDriftScan technology stack.

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