-- digna, the European data quality and observability platform, today announced the release of digna 2026.06, introducing a new Python SDK and Docker deployment support that make it easier for data scientists, data engineers, and developers to integrate data quality and observability directly into their existing workflows.
The release marks a significant step in digna's mission to make enterprise data quality, observability, and analytics more accessible to technical teams building modern data platforms, AI applications, and business intelligence solutions.
As organizations continue to invest in AI, machine learning, and data-driven decision-making, ensuring trusted data has become a strategic priority. The new Python SDK allows developers to interact programmatically with digna, automate inspections, manage configurations, and integrate data monitoring directly into Python-based workflows and applications.
Bringing Data Quality and Observability Into Developer Workflows
Until now, many organizations have managed data quality and observability through dedicated dashboards and operational processes. The introduction of the digna Python SDK enables teams to incorporate these capabilities directly into their development environments, CI/CD pipelines, analytics workflows, and custom applications.
With the SDK, developers can create projects, configure monitored tables, launch inspections, retrieve results, and automate observability processes without leaving their Python ecosystem.
"The way organizations consume data quality and observability is changing," said Marcin Chudeusz, Co-founder and CEO of digna.
"Developers and data scientists increasingly expect infrastructure platforms to integrate seamlessly into the tools and workflows they already use. With the Python SDK, we're making digna more accessible to technical teams and enabling them to embed trusted data practices directly into their daily work."
The SDK is distributed through the Python Package Index (PyPI), allowing teams to quickly install and begin working with digna using familiar development patterns.
Supporting Data Scientists and AI Teams
The release also strengthens digna's position as a platform for organizations building AI and advanced analytics solutions.
As machine learning initiatives continue to expand, data scientists face growing pressure to ensure the quality, reliability, and consistency of the data feeding their models. The Python SDK enables teams to integrate data quality and observability checks directly into model development workflows, helping identify issues before they affect downstream systems.
Data scientists can leverage digna's anomaly detection capabilities, automate monitoring tasks, and integrate observability results into broader analytics and machine learning processes.
Docker Deployment Adds Infrastructure Flexibility
Alongside the SDK, Release 2026.06 introduces Docker deployment support, giving organizations greater flexibility in how they deploy and manage the platform.
Docker-based deployment simplifies installation, testing, scaling, and environment management while supporting cloud, private cloud, hybrid, and on-premises infrastructure strategies.
This capability is particularly valuable for organizations operating in regulated industries where security, governance, and deployment control remain important considerations.
"Every organization has different infrastructure requirements," said Danijel Kivaranovic Co-Founder and CTO of digna.*
"By adding Docker deployment support, we're giving customers greater flexibility to deploy digna where it makes the most sense for their business. Combined with the Python SDK, this release makes it significantly easier to integrate data quality and observability into modern technology stacks."
Part of a Broader Platform Evolution
The 2026.06 release continues digna's strategy of extending observability beyond traditional monitoring and into broader business and analytics workflows.
The platform's modular architecture now includes:
- digna Data Anomalies – AI-powered anomaly detection for data quality and observability
- digna Data Analytics – Time-series analysis and business trend monitoring
- digna Data Validation – Rule-based data quality controls
- digna Timeliness – Monitoring data arrival and freshness
- digna Schema Tracker – Real-time schema change detection
Together, these capabilities help organizations improve data trust, reduce operational risk, and gain deeper insight into the business signals hidden within their data.
Availability
The digna Python SDK is available immediately through PyPI and is included as part of the digna Release 2026.06 platform update.
About digna
digna is a European data quality and observability platform that helps organizations improve data trust, reduce downtime, and uncover actionable business insights.
Its modular platform combines AI-powered anomaly detection, data quality validation, data timeliness monitoring, schema tracking, and business analytics to help organizations monitor and understand their data ecosystems.
Designed for enterprise environments, digna supports cloud, hybrid, and on-premises deployments and serves organizations across finance, healthcare, telecommunications, government, and other data-intensive industries.
Contact Info:
Name: Mayowa Ajakaiye
Email: Send Email
Organization: digna GmbH
Website: https://www.digna.ai/
Release ID: 89196086
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