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GoodData this week delivered on its promise to offer an enterprise edition of its headless business intelligence (BI) service. The company’s platform is built on a microservices-based architecture accessed via application programming interfaces (APIs).
The GoodData.CN Production platform builds on a previously released community edition to provide organizations with a platform for analyzing data that makes use of cloud-native technologies such as the open source Kubernetes container orchestration engine to enable it to scale, GoodData CEO Roman Stanek told VentureBeat.
This latest version of the platform is free for simple production deployments. More complex use cases for self-service analytics and large production deployments with enhanced features will be subject to licensing fees.
Most BI applications today trace their lineage back to monolithic personal productivity applications that require end users to deploy a lot of code, regardless of which function they invoke. A modern microservices architecture enables organizations to programmatically expose a more modular set of functions that are designed to scale up and down on demand, Stanek said.
That approach is fundamentally more efficient than deploying monolithic BI applications loaded with modules and features that no one actually uses, Stanek added. The GoodData.CN Production platform exposes those capabilities via a set of APIs that other applications can consume, rather than requiring an end user to navigate a separate user interface (UI). “There is no UI,” he said.
Pressure to embed analytics in every application is mounting as end users seek to make faster and better fact-based decisions. Rather than asking users to move data into a separate application to analyze it, the GoodData.CN platform promises to simplify the process of infusing analytics within an application that can be accessed in near real time.
The need to embed analytics within applications is becoming more pronounced as organizations seek to accelerate multiple digital business transformation initiatives. The expectation is that every application will soon include some type of embedded analytics capability that enables end users to make informed decisions in the moment rather than waiting on a business analyst to generate a report, for example.
As microservices-based applications become more widely deployed, the range of capabilities that can be programmatically invoked via APIs will continue to steadily increase. That approach should also provide the added benefit of shrinking the size of applications, as components that previously required code to be included in the application are now invoked via APIs.
A microservices-based application also makes it easier to add new features and capabilities to software by ripping and replacing the containers employed to construct it. And should a microservice become unavailable for any reason, API calls are dynamically rerouted to other microservices to ensure redundancy.
Independent software vendors such as GoodData are leading the microservices charge, but it’s only a matter of time before more enterprise developers employ microservices that allow organizations to more flexible consume software. The challenge, of course, is that microservices-based applications are more difficult to build and support, given all the dependencies that exist between the APIs being invoked.
In the case of BI, however, the shift to microservices should ultimately result in more usage of these applications across a larger base of end users invoking headless platforms from within any number of applications. Best of all, organizations will arguably have more control over their BI destiny because switching from one platform to another in the event something better becomes available will typically only require replacing a few APIs.
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