digna | Data Quality and Observability Software
digna helps data engineers and analysts identify data errors and anomalies. It is designed for teams that need to maintain data integrity within their existing database environments.
At a glance
- Category
- Data & Analytics
- Best for
- Mid-market companies, Enterprise companies, Data engineers, Data analysts, Organizations in regulated industries
- Pricing
- Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
- Key use cases
- Monitoring Data Pipeline Health, Automated Error Detection, Audit and Compliance Validation, Data Quality for AI Models
- Integrations
- Snowflake, Databricks, Teradata, PostgreSQL, Oracle
- Official website
- digna.ai
digna is a data quality and observability platform designed to help organizations identify patterns, errors, and anomalies in their data. The platform performs calculations directly within the user's database, which may be helpful for teams with security or data volume requirements.
The software is intended for data engineers, analysts, and business stakeholders. It focuses on five main areas: anomaly detection, historical analytics, timeliness monitoring, rule-based validation, and schema tracking.
Because it supports on-premise and private cloud deployment, it may be suitable for organizations in regulated sectors like finance and healthcare. Buyers should confirm that their specific database version is supported.
Key Features
Uses AI to learn normal data behavior and monitor for unexpected changes to help reduce manual rule maintenance.
Performs data analysis within the existing database environment so that data does not need to be moved.
Combines AI patterns with user schedules to detect missing loads, delays, or early deliveries.
Supports user-defined rules at the record level for business logic enforcement and audit compliance.
Monitors tables for structural changes, such as added or removed columns and data type changes.
Analyzes observability metrics over time to identify volatile metrics and statistical patterns.
Use Cases
Using Timeliness and Schema Tracker modules to identify issues caused by missing data or structural changes.
Applying AI-powered anomaly detection to identify unexpected data patterns.
Using record-level data validation to support business logic and regulatory requirements.
Monitoring data sources to support the reliability of Generative AI and LLM implementations.
Best For
Integrations
Pricing
Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
FAQ
digna performs calculations in-database, meaning data is not moved to the platform.
It is designed for data engineers, analysts, and business stakeholders, particularly in mid-market and enterprise companies.
It integrates with several databases, including Snowflake, Databricks, Teradata, PostgreSQL, Oracle, and SAP HANA.
The Data Anomalies module leverages AI to automatically learn normal data behavior and monitor for changes, which may reduce the need for manual rule maintenance.
Source category: Data & Analytics
Source subcategory: Data Quality
