digna introduces a fully modular, AI-powered approach that complements Teradata’s architecture without extracting customer data.
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.
Long-term trend analysis for observability metrics
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.
AI-driven and rule-based monitoring of data arrival times
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.
digna Data Validation provides:
This complements the AI modules by ensuring that every record meets defined business expectations.
Protecting pipelines from schema drift
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.