Actian Zen v16.10: Modern Observability, Enhanced Security, and Python Power

We’re thrilled to announce the release of Actian Zen v16.10, a significant update designed to modernize observability, strengthen data security, and empower Python developers.

Building on the innovative foundation of Zen 16.0, this release introduces three game-changing capabilities:

1. Prometheus-based telemetry.
2. Native SQL data masking.
3. SQLAlchemy dialect support.

These features are based directly on customer feedback to help your teams manage production deployments with greater visibility, protect sensitive information at the source, and build modern applications using standard Python tools.

Here is how Zen v16.10 helps you build smarter, safer, and more connected edge applications:

Modernize Observability With Prometheus Telemetry

For the first time, Actian Zen exposes a native/metrics endpoint, enabling standard Prometheus and Grafana workflows for monitoring engine health.

Previously, tracking performance on Linux deployments was a challenge, often requiring custom scripts or lacking the granular visibility available on Windows via PerfMon. With Zen v16.10, we leveled the playing field. You can now scrape real-time metrics—including cache hits/misses, I/O usage, lock contention, and transaction throughput—directly into Prometheus.

Why this matters:

• Unified Monitoring: Use the same Grafana dashboards to monitor Zen across both Windows and Linux deployments.
• Proactive Diagnosis: DBAs and SREs can set alerts for spikes in wait times or I/O, allowing them to resolve bottlenecks before they impact users.
• Standard Integration: No proprietary tools are required. If you use standard enterprise monitoring stacks like Splunk or Elasticsearch, you can easily feed Zen metrics into them via Prometheus.

Strengthen Security With SQL Data Masking

Data privacy is no longer optional for businesses. It’s mandatory. To help you meet strict compliance requirements, like GDPR, HIPAA, and PCI-DSS, Zen v16.10 introduces native Table Column Masking directly within SQL.

This feature allows administrators to obscure sensitive data—such as credit card numbers, social security numbers, or email addresses—so it cannot be seen by users who do not have explicit permission to view it. When an unauthorized user queries the database, they see masked values—either “xxxx” for all string-type columns or zero values for all numeric types in the results, while the actual underlying data remains unchanged.

What this enables:

• Granular Access Control: Define visibility at the column level. A customer support agent might see only the last four digits of a credit card, while a finance manager sees the full number.
• Zero Application Changes: Implement security rules at the database layer. You don’t need to rewrite your application code to handle masking logic.
• Non-Destructive Safety: The underlying data remains intact for authorized processes, ensuring that analytics and reporting jobs still run accurately, but the data is protected from human eyes that don’t have authorization.
• Universal Enforcement: Because masking is applied at the engine level, it applies to third-party ad-hoc access as well. Anyone connecting via a SQL report writer, like Tableau or Excel, will see the same masked data, preventing users from bypassing security by switching tools. 

Build Faster With SQLAlchemy Support for Python

Python is the language of data, and Zen v16.10 now fits naturally into the Python ecosystem with official SQLAlchemy dialect support.

Developers can now use Zen as a backend for popular ORM-based applications. If you are hitting the concurrency limits of SQLite but don’t want to rewrite your entire application logic, Zen v16.10 is the perfect upgrade path. You can keep your SQLAlchemy models, queries, and migrations intact—just change the engine URL.

What this enables:

• Drop-in Compatibility: Use standard tools like Pandas, Polaris, and Dask directly with Zen.
• Higher Concurrency: Move beyond SQLite’s single-writer limitations to a robust, multi-user embedded database without managing a full server.
• Developer Velocity: Focus on building features using Python objects rather than writing database-specific SQL queries.

Ready to Upgrade?

Actian Zen v16.10 is available now. Whether you need better visibility into your production engines, enhanced security controls for sensitive data, or a more powerful backend for your Python apps, this release delivers the tools you need to succeed at the edge.

About Author

About Tahiya Chowdhury

Tahiya Chowdhury is the Product Manager for Actian Zen, where she leads the strategy for the industry's most robust edge data platform. Drawing from her background at MongoDB and Goldman Sachs, Tahiya specializes in building products that sit at the intersection of high-scale enterprise needs and modern developer velocity. She is passionate about removing the complexity from data infrastructure, empowering engineering teams to move faster from prototype to production.