Open Source Denodo Alternatives

Cube Core is a standalone semantic layer. Metrics, dimensions, joins and access rules are defined once in code and then exposed through SQL, REST and GraphQL to anything downstream, which ends the habit of re-implementing the same business logic inside every reporting tool that touches the warehouse.

It is headless and ships no interface of its own, leaving the analytics experience to you. It connects to SQL data sources including Snowflake, Databricks and BigQuery, query engines such as Presto and Amazon Athena, and application databases like Postgres. A built-in relational caching engine gives sub-second latency and high concurrency for API requests.

The case for a separate layer comes down to a few points.

• Reusable definitions: most semantic layers are proprietary and welded to one BI platform, so the model cannot travel.
• Standard APIs: internal BI, embedded analytics and AI agents all read the same model over the same interfaces.
• Governed foundation: access rules sit with the metrics instead of being rebuilt in each downstream tool.
• Caching: the relational cache absorbs concurrent requests that would otherwise land on the warehouse.
• Model portability: a model built here runs unchanged on the commercial Cube platform, and the reverse holds too.

Running it locally or self-hosting takes a Docker container with your project folder mounted and a port published for setup. It fits teams that want to own the stack, whether that means a custom BI experience, deeply integrated embedded analytics, or agents that need a governed foundation of metrics to reason over.

Orbital automates the integration between your data sources. There is no central mapping layer to write. It reads the API specs your teams already publish and builds each integration at query time, which removes the stitching code that breaks whenever one of those APIs moves.

The metadata comes from Taxi, a language that embeds semantic types in OpenAPI, Protobuf, Avro and JSON Schema documents, and Orbital itself is a TaxiQL query server. Specs reach it by polling, from a Git repository, or pushed by a service on startup. A query describes the response you want and Orbital composes the calls that produce it.

The project gives several reasons for working this way.

• No glue code: integration code between APIs is brittle and breaks every time one of them changes.
• Spec first: existing API specs drive the integration, leaving less code to maintain.
• Technology agnostic: REST, gRPC, SOAP, Kafka and databases are all treated the same way.
• Automatic adaptation: as specs change, integration flows adjust and consumers are left alone.
• Resolver free: unlike GraphQL federation, there are no resolvers for a middleware team to maintain.
• Decentralized: teams evolve independently, without sharing identifier schemes or common models.

Getting started is a docker compose file fetched from the project's start page, then a browser pointed at the local port it exposes. Orbital fits an organization that wants federation across mixed technology without moving its stack to GraphQL.