InetSoft Webinar: Building New Cloud BI Applications
Below is the continuation of the transcript of a Webinar hosted by InetSoft on the topic of the Power of Data with Embedded Analytics Tools. The presenter is Abhishek Gupta, Chief Data Scientist at InetSoft.
Even in established organizations that have established on-premises systems they're looking at building new cloud BI applications and particularly around analytics often to take advantage of the data gravity. The gravity being that lots of the data already in the clouds.
They want to move analytics to the cloud and take advantage of it, as I mentioned here, point technology using in database processing. You've got powerful platforms out there. Why not use them effectively for churning through analytics and even for data preparation, ETL kind of activities to support analytics. We see a lot of trending towards that being able to use the parallel processing and more powerful platforms in the cloud for this.
Then the other thing to mention about Cloud of courses is it's not one Cloud but many Clouds and multi-cloud. We see most organizations are using the big ones: Amazon, Microsoft, Google, but certainly even others, multi-clouds sometimes. It just works out that way that they're moved into the Cloud in different parts of the organization, moved to different Cloud systems, or it's by design because they want to avoid vendor lock-in and platform lock-in and have some flexibility there.
Portability and Multi-Cloud Support
This is certainly a feature of the Cloud these days that it isn't necessity of having analytics. It will be portable and going to solutions that can support multiple different clouds so not just one, but certainly the big ones out there if not others as they come along. Take advantage of the Cloud infrastructure.
Here I'm kind of grouping together what's going on really: going into the development world which is certainly changing quite a bit around Cloud. This is around the use of embedded BI systems and or rather embedded analytics in API containers and virtualization and really moving into this kind of a more virtual world around using microservices and things like that for managing and orchestrating the use of containers.
We're starting to see embedded analytics systems begin to make use of that in the Cloud. First we'd talk about really just the Cloud platform impact and why that's important in terms of the power and the scalability and the dynamism. But now we're looking at how these applications are constructed and how they're put together and interact with each other.
APIs and Containerization
This is a whole new world obviously. It's changing quite a bit in the development of these systems. Use of APIs that's something that has been around forever with service oriented architectures and client server computing. But now more are using standard systems around JavaScript and Rest and so forth to enable the systems to come together, for even data to be able to flow between them more easily, and to improve the containerization.
Basically there are features and functionality that can now more easily put together and integrate and as sort of as components to create the impediment bedded analytics. This is a very important development in analytics. Using APIs containers, using a more virtualized kind of idea around containers and then using the standard's very important so that it's easy to plug in different functionality that you need.
Benefits of Cloud BI for Embedded Analytics
Then the other benefit about the cloud is also being able to control releases and upgrades. We certainly are seeing that with commercial BI software vendors, but there's no reason why organizations themselves as they manage embedded analytics couldn't be doing the same and being able to do it more effectively from a centralized posture, rather than having to go to each individual embedded information system, which takes a tremendous amount of time and effort from IT standpoint.
It's great for third party developers and OEM providers which have adopted embedded BI solutions. As the third party developers have accumulated a particular expertise, a subject matter expertise or business process. Well there's increasing demand for those kinds of third party systems to have intelligence and insight, to be able to have the kind of use of data that's effective through dashboards, through a drag and drop visual interfaces, to provide access to different types of data than may have been in traditional embedded systems, to have more, in other words, more breadth around the data.
As organizations continue modernizing their analytics stacks, one of the most significant architectural shifts is the move toward decentralized data consumption paired with centralized governance. Cloud BI applications increasingly rely on shared semantic layers that allow developers to define business logic once while exposing it across multiple dashboards, microservices, and embedded contexts. This approach reduces duplication, ensures consistency, and accelerates development cycles—especially in multi-cloud environments where data may reside across several platforms. InetSoft’s modeling layer supports this pattern by enabling reusable data worksheets that can be versioned, governed, and deployed as part of a CI/CD pipeline.
Emerging Trends in Cloud BI Application Development
Another emerging trend is the adoption of event-driven analytics pipelines. Instead of relying solely on scheduled refreshes or batch ETL, cloud BI applications are beginning to incorporate streaming ingestion from message queues, IoT devices, and operational event logs. Developers can use these streams to trigger incremental updates, refresh specific dashboard components, or feed real-time alerting systems. InetSoft’s ability to blend streaming and static datasets within the same analytical model allows developers to build dashboards that react to operational changes as they occur, without overwhelming backend systems or requiring complex custom code.
Containerization also plays a major role in how modern BI applications are deployed and scaled. By packaging analytics services into lightweight containers, developers can orchestrate workloads dynamically based on user demand, data volume, or scheduled processing windows. This is particularly useful for embedded analytics scenarios where usage patterns vary widely across tenants. InetSoft’s support for containerized deployments allows BI components to scale independently from the host application, ensuring that heavy analytical workloads do not degrade the performance of transactional systems.
Security and identity management have likewise evolved in the cloud BI landscape. Developers must now integrate analytics with federated identity providers, enforce row-level and column-level security, and ensure compliance across multiple regulatory frameworks. Cloud-native authentication standards such as OAuth2 and OpenID Connect make it possible to unify access control across embedded dashboards, APIs, and data services. InetSoft’s security model aligns with these standards, enabling developers to map user roles, tenant boundaries, and data entitlements directly into the BI layer without writing custom authorization logic.
Finally, the rise of low-code and no-code extensibility within BI platforms is reshaping how development teams collaborate with business users. Instead of relying exclusively on engineers to build dashboards and data transformations, cloud BI applications now empower analysts and domain experts to contribute directly to the analytics lifecycle. Developers can expose curated datasets, reusable visual components, and governed templates that allow non-technical users to assemble dashboards safely within defined constraints. InetSoft’s drag-and-drop environment supports this hybrid model, enabling organizations to accelerate delivery while maintaining the architectural discipline required for scalable cloud analytics.