Digna

The tools you use should never limit the quality of the data you trust.

With the digna Python SDK, data teams can now build, automate, and integrate data quality workflows directly within their Python environments.

Whether you’re validating datasets, embedding quality checks into pipelines, or scaling observability across complex architectures, the SDK gives you the flexibility to work the way modern data teams do, programmatically and efficiently.

Because data quality shouldn’t be an afterthought. It should be built into every workflow from the start.

Click the link below to learn more.
🔗: https://www.digna.ai/digna-release-2026-06-data-observability-into-your-code

Reliable public services depend on reliable data.

Over the years, we’ve seen this firsthand through projects with public-sector organizations such as IT-Services der Sozialversicherung GmbH where data quality, consistency, and observability play a critical role in large-scale government systems.

This article explores why modern data governance in the public sector increasingly depends on strong data quality foundations.

Read more 👇

Data Reliability in Government: How Public Agencies Can Build Citizen Trust Through Data Quality Discover how government agencies can improve public sector data governance and data quality to strengthen service delivery, meet compliance requirements, and rebuild citizen trust.

A lot of the conversation around Generative AI focuses on models.

Far less attention is given to the quality of the data those models consume.

But even advanced LLMs become unreliable when the underlying data is incomplete, inconsistent, delayed, or structurally unstable.

In this article, we explore why clean, observable data is becoming a critical requirement for reliable AI deployment, and how data quality issues propagate directly into AI outputs.

Read more 👇

https://www.digna.ai/feeding-llms-clean-data-generative-ai-deployment

Feeding LLMs with Clean Data: What Generative AI Teams Must Get Right Before Deployment Even the most advanced LLMs fail when trained on poor-quality data. Discover the critical data quality practices generative AI teams must implement before deploying models to production.

Most teams can see their data.

Far fewer can explain what it’s actually doing over time.

Understanding trends, patterns, and anomalies has traditionally required data science workflows, which creates a bottleneck for many organizations.

This is starting to change.

In this article, we explore how time-series analysis and anomaly detection are becoming more accessible, allowing business users to understand data behavior without relying on specialized tools or teams.

Read more 👇

digna Democratizes Time Series Analysis and Anomaly Detection for Business Users Learn how digna enables business users to perform time series analysis and anomaly detection without data science tools or coding.

Most teams don’t struggle with data.

They struggle with understanding how it changes over time.

digna 2026.04 introduces built-in time-series analytics:
→ Trend detection
→ Seasonality analysis
→ Pattern shifts
→ Deviation insights

All inside your database. No data science required.

Because knowing what changed isn’t enough.

You need to know why.

Learn more >> https://docs.digna.ai/changelog/Release_202604/

📢 digna 2026.04 is live!

We’ve expanded digna from monitoring data…
to helping teams actually understand it.

This release introduces:
→ Time-series analysis inside the platform
→ Reusable data validation components
→ Smarter, context-aware monitoring

No Python. No external tools. No data movement.

Just data clarity — where your data already lives.

👉 Explore what’s new: https://www.digna.ai/digna-2026-04-self-service-time-series-analytics-business-users

Introducing digna Release 2026.04 — Bringing Time-Series Analytics and Scalable Data Validation to Every Team digna Release 2026.04 extends Data Analytics with a self-service interface that lets business users explore time-series quality metrics independently, without Python, SQL, or data scientist support.

Most Databricks monitoring tells you whether jobs succeeded. It doesn't tell you whether they're becoming more expensive, more volatile, or less predictable, quietly, over months.

Six root causes drive the majority of Databricks instability in production environments:

✅ Data volume growth altering Spark ex*****on plans without any code change
✅ Incremental logic additions compounding overhead over time
✅ Auto-scaling absorbing inefficiencies silently
✅ Data skew producing unstable runtimes across otherwise identical runs
✅ Task retries inflating DBU consumption without any visible error
✅ Workload seasonality misread as anomalies (or genuine anomalies missed as seasonality)

The monitoring approach that catches these isn't threshold-based. It's behavioral, learning what normal looks like per job, per context, and detecting meaningful deviation before it shows up as a cost spike.

We published a detailed breakdown of how this works. Worth a read if you're running Databricks at production scale.

Most data pipeline failures are not caused by bad code.

They’re caused by schema changes that no one noticed.

✅ A column disappears.
✅ A datatype changes.
✅ A table structure evolves.

Downstream systems continue running until something breaks — often hours or days later.

Schema tracking helps data teams detect structural changes early, before they propagate through pipelines, dashboards, or machine learning workflows.

digna Schema Tracker monitors schema evolution across databases so teams can see what changed, when it changed, and where it impacts downstream data systems.

Because stable analytics depends on stable structures.

Visit our website to learn more.

Data quality issues rarely come from a single broken record.

They usually appear when rules that should exist… don’t.

In many enterprise data platforms, teams need to enforce structural rules such as:
✅ uniqueness of business entities
✅ referential integrity between datasets
✅ row-level validation across tables

Without these checks, duplicates, broken relationships, and inconsistent records can quietly propagate through analytics and reporting.

digna Data Validation allows teams to define and run these rules directly where the data lives, helping enforce data integrity at scale across complex environments.
Because reliable analytics always starts with reliable data.

Learn more at digna.ai/data-validation

Our latest platform release got featured on Yahoo Finance.

The article highlights how digna 2026.01 strengthens enterprise data quality and observability, introducing expanded validation capabilities such as multi-column uniqueness checks and referential integrity validation.

The release also advances our approach of running data quality checks directly inside the source database, helping organizations enforce data integrity without moving data outside their environments.

As data architectures grow more complex, enterprises need data quality and observability that work where the data already lives.

Read the coverage on Yahoo Finance 👇

https://finance.yahoo.com/news/digna-introduces-major-platform-enhancing-154200893.html?guccounter=1

digna Introduces Major Platform Update Enhancing Enterprise Data Quality and Observability digna's new release adds global database connections, multiple source connections per project, logical datasources, anomaly relevance conditions, module-level notification settings, and CSV exports. It also expands data validation with multi-column uniqueness and referential integrity checks.Vienna,...