Manage your data with a Dagster pipeline and Cube Semantic Layer

How Cube and Dagster work together

Integrate Cube with Dagster to seamlessly connect your data transformation pipelines and orchestration tools with your semantic layer. Handle all of your rough-grained transformation tasks with Dagster and land data in your warehouse and then perform your business logic and metric definition in Cube and provide consistent analytics throughout the organization. Use event-based integrations to trigger your pre-aggregation builds efficiently and easily, saving time and compute resources.

Power your data applications with Dagster and Cube

Performance + Reliability

Redundantly and directly querying your data source results in latency and unnecessary costs. Cube’s caching layer, pre-aggregations, and query queuing fix that.

Skip latency;
expect speed.

Headless = Customized

Decoupling BI from your front-end means orchestrating data modeling, access control, and caching once—while building presentation layers for any use case.

Easily tailor your
user experience.

Security + Governance

Reconfiguring data access control for every data app in your stack opens the door to inconsistencies and gaps. Centralize permissions upstream for uniform security.

Maintain ubiquitious
access control.

Step-by-step guides on using Cube with Dagster

Tutorial

Introducing Orchestration API

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Check the issues

250 contributors and 14.1k stars on GitHub

See Cube in action

How Relata built completely native embedded analytics that runs 85% faster.

Read the Case Study

How RamSoft built its users native embedded analytics—in two weeks.

Read the Case Study

Cube helped us reduce response times 100x and increase the amount of information we could display on our customer-facing dashboards.

Thomas Ankcorn

Technical Lead at NearSt

With Cube, we’ve been able to speed up time to release a new data model to production by 5x and decrease analytics downtime by 90%.

Alessandro Lollo

Senior Data Engineer at Cloud Academy

We can develop and ship analytics products without dealing with any net new infrastructure development going forward. Our efforts are focused on what matters: the data and the UI.