A Sovereign Databricks Alternative
Where Databricks is a lakehouse built for data engineering and ML, Scrydon adds a business-level ontology and European data sovereignty on top of an open lakehouse — so business users and AI agents work from trusted meaning, not just raw tables.
Ontology, Not Just a Catalogue
A first-class business ontology and semantic layer over the lakehouse — meaning defined once, not re-derived per team.
For Business Users & Agents
Analysts and AI agents reason on governed business meaning, not raw tables and notebooks aimed at engineers.
Sovereign & Open
Open table formats inside your own perimeter, European-native, air-gapped to cloud — not a managed service on a US hyperscaler.
Scrydon is a sovereign alternative to Databricks: an ontology based data platform that pairs an open lakehouse with a first-class business ontology and a semantic layer. Where Databricks targets data engineers and ML teams working over raw tables, Scrydon lets analysts, business users, and AI agents reason on consistent business meaning — all inside your own perimeter, European-native, from air-gapped on-premises to cloud.
Databricks is an excellent lakehouse for data engineering and machine learning, but it leaves meaning to each team to re-derive: a catalogue and metric views, not a business ontology. Scrydon keeps the open-lakehouse foundation — built on open table formats such as Apache Iceberg — and layers a native ontology and semantic model over it, so metrics are defined once and every report, dashboard, and agent agrees on them. And it runs sovereign and European-native, with your keys and your perimeter, rather than as a vendor-managed service on a US hyperscaler.
Databricks Alternative in the Scrydon platform
One integrated, sovereign architecture. Here is where Databricks Alternative sits — highlighted against the full stack it works with.
The AI OS for Humans & AI Agents to enable your processes
df.plot.bar()
Link your processes, knowledge & data to ontologies.
Unified storage, structured compute, and secure multi-modal data processing.
Autonomous operatives with specialised skills executing tasks across systems.
Sovereign pipelines, federated APIs, and seamless connector meshes.
Secure domain federation, trusted data sharing, and cross-boundary intelligence.
Databricks Alternative in depth
Human + AI Orchestration
The AI OS for Humans & AI Agents to enable your processes
The Human + AI Orchestrator is the operational runtime at the heart of the AI OS — scheduling, routing, and governing every task across your enterprise, whether executed by an AI agent, an existing system, or a human.
Most organisations have broken processes: encoded in siloed systems or locked in people's heads. The AI OS makes them visible and executable. It captures intent, synthesises context, acts — then feeds every result back into the ontology so the next run is smarter. All of it inside your perimeter.
Insights
Data sitting in warehouses and dashboards that nobody reads is data they can't use. The Insights layer changes that — giving the right people the right information without them having to ask for it. Every metric is anchored to the Cognitive Enterprise ontology, so a revenue figure doesn't arrive in isolation. Data in context — not just in dashboards.
Decision-makers get a live view of the enterprise — financial performance, operational health, procurement status — without waiting for a data team to prepare a report.
- Interactive notebooks: Python and SQL environments with full access to your lakehouse data — no data movement required.
- Visual dashboards: Pre-built, always-current reporting updated automatically as the business moves — no manual refresh, no stale numbers.
- Agent-native analytics: AI agents can query, summarise, and act on insights autonomously — closing the loop between analysis and action.
Cognitive Enterprise
Link your processes, knowledge & data to ontologies.
Most organisations have data they can't use — not because it doesn't exist, but because nothing connects it. The Cognitive Enterprise layer is the defining intelligence of the AI OS: a living, queryable semantic model of your organisation's entities, processes, and rules. It is the single source of truth that allows every agent, analyst, and workflow to reason about your business with a consistent understanding.
Without it, AI agents reason on noise. With it, they reason on the business.
- Entity graph: Model customers, accounts, orders, products, and any domain concept — then connect them with typed, traversable relationships.
- Process integration: Link real-world workflows to ontology entities so agents understand how data flows through your business.
- Continuous enrichment: Agents automatically enrich ontology nodes with fresh data from the lakehouse, keeping the model current without manual effort.
The Lakehouse is the high-performance data foundation underpinning the Cognitive Enterprise. It is built on StarRocks — a blazing-fast, vectorised MPP query engine delivering sub-second analytics, real-time updates, and high concurrency — and queries open Apache Iceberg tables directly, merging the flexibility of a data lake with the speed of a warehouse under a single, sovereign roof.
- Open Iceberg tables: Query Apache Iceberg and other open table formats directly — your data stays yours, with no proprietary lock-in and no data movement.
- Lightning OLAP: StarRocks' vectorised engine, cost-based optimiser, and materialised views power real-time SQL — from dashboards to agent reasoning — without data duplication.
- Integrated Vector Search: Store and query embeddings alongside traditional data, making the Lakehouse instantly ready for AI workloads.
Pipelines
Broken processes live in the gaps between systems. The Integrations layer of the AI OS closes those gaps, connecting securely and seamlessly to the operational tools you already rely on.
We provide a vast library of built-in integrations for immediate connectivity to standard CRMs, databases, and enterprise applications.
To ensure maximum flexibility, the platform natively supports open standards—including OpenAPI, MCP, and A2A. This standard-first architecture makes it incredibly easy to build and deploy custom integrations, allowing the full platform to interact with any proprietary system or specialized tool within your infrastructure.
A lakehouse with meaning on top
Scrydon keeps the open lakehouse that makes Databricks powerful and adds the layer it lacks: a business ontology. Instead of engineers wiring up metrics in notebooks for every question, entities, relationships, and definitions are modelled once and shared by analytics, insight, and AI — so the platform serves the whole organisation, not just the data team.
Open lakehouse — Built on open table formats such as Apache Iceberg with high-performance SQL — your data stays portable and in place.
Native ontology — A first-class semantic model of entities, relationships, and rules over the lakehouse — not just a catalogue and metric views.
Consistent metrics — Define each metric once so dashboards, reports, and agents reconcile across the organisation.
Sovereign deployment — Run inside your own perimeter, European-native, from air-gapped on-premises to cloud, with your keys.
From engineering platform to organisational platform
Databricks is built for engineers and data scientists; getting trustworthy answers to business users still means custom pipelines, notebooks, and re-derived metrics, run as a managed service on a hyperscaler. A sovereign, ontology-first alternative shifts the centre of gravity to shared business meaning: analysts self-serve, agents stay grounded, metrics reconcile, and everything runs inside your perimeter under your control — without giving up the open lakehouse beneath it.
Scrydon vs Databricks
Both build on an open lakehouse. The difference is the layer above it: Scrydon adds a business ontology and semantic layer, and runs sovereign and European-native.
| Capability | Scrydon | Databricks |
|---|---|---|
| Primary focus | Ontology-based data unification and trusted insight | Lakehouse for data engineering and ML |
| Semantic / ontology layer | Native, first-class business ontology | Limited — catalogue and metric views, not a business ontology |
| Primary users | Business users, analysts, and AI agents on shared meaning | Data engineers and data scientists |
| Deployment & sovereignty | Sovereign — air-gapped to cloud, European-native, your keys | Cloud (AWS / Azure / GCP), vendor-managed |
| Openness & lock-in | Open formats, your perimeter, low lock-in | Open formats (Delta, Iceberg interop); platform-centric tooling |
| Best fit | Organisations needing a sovereign semantic layer for insight and AI | Data engineering and ML at scale |
Comparison is Scrydon's summary for orientation. Databricks is a trademark of Databricks, Inc.; capabilities evolve — verify current details with the vendor.
Frequently asked questions
What is the best alternative to Databricks?+
How is Scrydon different from Databricks?+
Does Scrydon replace the lakehouse?+
Is Scrydon more sovereign than Databricks?+
Can business users and AI agents use it directly?+
Explore the platform
Email us
Prefer to write? Email hello [at] scrydon.com and we will get back to you.
Partners
Building the future of Data & AI together with leading innovators. Learn more .
