Combine and consume

Headless BI engine

Build consistent, cloud-native analytics with an API-first approach. Combine all your data into a single source of truth and consume it anywhere, in real time.

What is the headless BI engine?

The headless BI engine is a key part of GoodData’s analytics platform. It is built to scale with containerized microservices, managed as a code, and to separate the analytical backend from the consumption layer. You can connect any application, data platform, or visualization tool to the engine's semantic layer — via APIs and standard interfaces — and consume the same consistent analytics in real time, anywhere.

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Building blocks of the headless BI engine

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Actionable data for everyone

Universal semantic layer

The semantic layer standardizes your analytics for all end users and downstream applications. It transforms complex data into easy-to-understand, reusable semantic definitions, such as “product,” “customer,” or “revenue.” These simplified definitions allow your business users to interact with data autonomously without in-depth knowledge of the underlying data structures.

A single source of truth

Build a centralized and governed place to define, manage, and store all your semantic definitions — accessible from anywhere.

Built-in resilience

Change the underlying physical data or the source data structure without breaking the semantics or downstream analytics.

API-first approach
Stronger analytics with APIs

API-first approach

All engine functions can be performed via well-designed open APIs with declarative definitions. Everything you build and configure is both human- and machine-readable, so you can manage your analytics as code. Use APIs to access and present data from the semantic layer to any application — or automate, version, and extend your entire analytical solution.

Automated development

With open APIs, you can automate every stage of your analytics — creation, testing, and provisioning — and create generators for various programming language bindings.

Effective and efficient collaboration

The declarative metadata enables data teams to work in parallel to version, merge, test, and roll out analytics, without fear of interfering with someone else’s work.

Composable and reusable metrics
Company-wide consistency

Composable and reusable metrics

Unlike queries, composable metrics can be reused in other metrics and different contexts. The semantic layer takes care of joins, sub-joins, and GROUP BYs, and the engine automatically generates SQL — so you don't need to write thousands of SQL queries. Instead, compose all of the metrics from a few baseline components to ensure consistent results.

End-to-end metrics management

Create and manage metrics in a single metrics store. Changes made to any metric will automatically be applied to every related metric composition, dashboard, and application.

Automated, real-time query generation

The engine translates all metrics to core algebraic operations arranged in a hierarchical query tree and automatically generates its own SQL queries in real time.

No advanced SQL skills needed

Business users can write new metrics or combine existing ones using simple expressions directly in the GUI or via APIs.

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Advantages of the cloud

Cloud-native architecture

The engine is built on microservices and packaged in lightweight containers to be deployed and dynamically orchestrated across various servers to optimize resource utilization. Build and run scalable analytics — with built-in elasticity reacting to data volume and user traffic — in modern environments such as public, private, and hybrid clouds.

Cloud platform agnostic

Deploy via Docker and Kubernetes to any cloud or combination of clouds, and — if necessary — migrate to another vendor without technical incompatibilities.

Zero downtime releases and updates

Manage the engine using continuous integration, and make high-impact changes frequently and with minimal effort.

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Native integration with your data stack

Open integration

The engine integrates seamlessly with your data sources, authentication protocols, monitoring and logging tools, CI/CD infrastructures, front-end frameworks, and more. The open integration allows you to leverage your existing data stack components and add others according to changing requirements.

Leading and distinctive composability

Choose the best tools, applications, and frameworks to compose the optimal combination of data components for your data infrastructure.

Future-proof resilience

Open integration provides the ability to adapt to future technologies and frameworks.

Dive deeper into the headless BI engine