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The field of Business Intelligence has significantly evolved over the last decades. While in the 1980s, it was considered an umbrella term for all data-driven decision-making activities, these days, it’s most commonly understood as solely the “visualization” and analytics part of the data lifecycle. Therefore, the term “headless BI” seems to be an oxymoron: how can something which inherently serves visualization be headless? The answer is thanks to the API layer.
The ultimate goal of Business Intelligence is to leverage data to guide business decisions and measure performance. Therefore, it’s essential to approach it strategically by following engineering best practices and building future-proof data ecosystems.
Historically, monolithic tightly coupled applications often proved to be difficult to scale, develop, redeploy and maintain. To make any changes to a single element of the system, one would need to affect all other components by redeploying a new version of the application.
Microservices emerged as a popular design choice allowing to decouple such monolithic architecture into independent microservices. Loosely coupled microservice architecture implies a collection of individual autonomous components potentially unaware of each other. Those components typically perform a small amount of work and thus contribute to potentially greater output. By using decoupled components, we try to minimize dependencies in the architecture— each element of the system should be able to work by itself, as well as to communicate with other components by means of standardized protocols.
By applying the same principles to Business Intelligence, we may, for instance, break large dashboards into individual charts, metrics, and insights that can be (re)used in various reports. Several BI vendors approached it by introducing a semantic layer that provides a common definition of metrics that can be shared across reports. What are the benefits of that approach?
- facilitating reuse of components → the same insight can be reused across many different dashboards,
- eliminating update anomalies → the insight or KPI needs to be defined and updated only once,
- preventing duplicate efforts → if somebody already created a specific metric and shared it, we can avoid building it for the second time,
- eliminating conflicting KPI definitions → by sharing the same definition of metrics, we can provide a single source of truth to analytics as this reduces the risk that the same KPI can be defined differently in several places.
As mentioned in the introduction, the term headless BI seems to be an oxymoron at first. But as long as the BI architecture is built in the API-first way, headless BI can be accomplished. At first, you may shrug and say: well then, everything is now behind an API, so what? Nothing changes, right? Not quite. By building BI software on top of a well-designed API, you are opening doors for all sorts of automation, versioning, easier backups, access control, and programmatically scaling your architecture to new customers and domains. For instance, you can:
a) Share specific analytical applications with external stakeholders.
Imagine that you need to share some dashboards with your external partners. The API-based approach allows you to assign specific fine-grained access permissions and limit the scope of what those users can do with the analytical application (for instance, see only specific areas or having read-only access).
b) Easily move from development to production
The declarative definition of the analytical application allows you to build your charts and metrics in the development environment. Once everything is thoroughly tested, you can move to production by exporting the declarative definition file and importing it into a new environment with just a few API calls.
c) Apply GitOps and Infrastructure as Code to your BI applications
Just as you would approach any software development project, an API-based BI stack allows you to version-control your dashboards and KPI definitions for reliability. If you notice that a new KPI declaration seems to be wrong at some point, you can roll back to the previous version. This way, you can additionally track how your metric and dashboard definitions change over time. Finally, if somebody accidentally changed or deleted a chart or dashboard, you can recover from it by recreating the most recent version from a Git repository.
d) Build analytical applications that can be deployed as a service
If you want to build a custom front-end application, the microservice approach allows you to do that. An example of how this can be accomplished:
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To sum up this section, with the API-first approach, the sky’s the limit. Almost everything can be automated, versioned, and extended.
In this article, we looked at the benefits of bringing microservice architecture to a BI stack. We looked at the semantic layer in the data modeling process and how decoupling can help make analytical processes more resilient. Finally, we used the community edition of the brand new cloud-native GoodData.CN platform that provides a fully-fledged BI platform in a single Docker container. This setup is great for development, non-production, and evaluation processes, but if you want to use it for production, you should look at the Helm chart Kubernetes deployment. Or, if you are considering a fully hosted solution instead, look at the technical overview of the GoodData platform.
Thank you for reading!