Marcin Chudeusz

Only metrics are exported — processing happens inside your Teradata system.

Below are the modules most relevant for enterprise Teradata workloads.

AI-powered anomaly detection for volumes, distributions, outliers & missing data

As workloads scale, Teradata tables can shift subtly over time — and without visibility, these issues reach end users or models too late.

digna Data Anomalies automatically learns:

When something deviates beyond AI-learned expectations, digna notifies teams before problems escalate.

Perfect for Teradata environments that:

This replaces hundreds of static rules with a single AI-powered monitoring layer.

Teradata’s workload patterns evolve over months and quarters. digna Data Analytics evaluates trends over time to detect:

These insights help platform teams:

This is especially impactful in Teradata’s massive, multitenant data environments.

SLA breaches are a common pain point in Teradata-powered analytics.

digna monitors:

Its AI model learns normal behavior instead of relying purely on a static SLA definition.

A rule-based layer for strict compliance and audit requirements

Some industries (finance, insurance, telecommunications, healthcare) require explicit, enforceable rules.

This complements the AI modules by ensuring that every record meets defined business expectations.

Teradata environments often support hundreds of pipelines. A single schema change can break dozens of downstream jobs.

digna automatically tracks:

When drift occurs, teams are alerted immediately, preventing silent pipeline failures.

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