Does The Embedded Analytics Software Support My Architecture Or Tech Stack?

Does the Embedded Analytics Software Support My Architect or Tech Stack?

In the last chapter of our buyer’s guide to purchasing embedded analytics software, we broke down how embedded analytics software is licensed and how much those licenses usually cost. In this chapter, we’ll explore how to determine if a prospective platform can support your company’s architecture and tech stacks.

Does the Embedded Analytics Software Support My Architecture or Tech Stack?


In this chapter, it might help to brush up on the following definitions:

Tech Stack – A group of solutions, tools, and software that form a data ecosystem required to run a single business or application.
BI – Business intelligence. The processes companies use to build strategies and tech that can analyze business data for several use cases.
NoSQL – A type of database that uses a mechanism for data movement that is modeled in something other than traditional relational databases.

Here’s What You Need To Know

What type of BI platform are you looking for in terms of hosting?

Are you looking for a business intelligence platform that you can host on your private cloud or a cloud-hosted solution? There are several pros and cons associated with each option. A pre-hosted solution will be more expensive. Just as well, you may have to deal with forced software upgrades and downtime that isn’t in your control. However, the responsibility for hosting will be put on the vendor and not your business. If your IT team is limited, this might be the best possible solution. If you opt for a vendor-hosted product, ensure that their internal data security is extremely strict and vetted.

Does the prospective solution use third-party tools that might conflict with your tech stack or base system?

Many BI platforms integrate some embedded databases and database requirements for installation, such as Microsoft SQL, Oracle, etc. Unfortunately, you might not have these database capabilities in your architecture. Ask your prospective vendor if the interface will utilize things like that Java or open-source frameworks must reference in licensing documentation.

What operating system and architecture are required?

Compatibility is key when choosing an embedded analytics software product. For example, if you use a Linux-specific environment, it will probably not be worth the cost and work-around of running a Windows server architecture on your platform. You might be looking at a product written in .Net or C#, but your architecture is based on windows. Is it worth learning how to become a Linux administrator to support this product? In most cases, you’ll be better off choosing a product that matches your operating system and core architecture.

How does reporting align across multiple platforms?

When looking at a prospective product, ask yourself: Is the product designed to be responsive and mobile-friendly? Does the product have APIs or SDK? Can the product use iFrame and Javascript embeds? Try to align your reporting processes with the product you choose.

Does the software support your data source and endpoints?

Most SaaS vendors don’t use traditional databases anymore, as there are newer and more flexible solutions available nowadays. However, most business intelligence platforms will still heavily rely on traditional databases. If your company uses NoSQL databases strictly or relies on reporting on a restful API layer, be sure to find out how your potential product can handle non-traditional and traditional database operations.


Know what kind of hosting you want, ensure no conflicting third-party tools are involved, determine if your operating system and architecture align with the product, and ensure that your reporting needs and endpoint needs align with the product.

The biggest takeaway of this chapter is to get all of the facts about the product up-front, so you won’t waste your time and money dealing with vendors who cannot offer you exactly what you need.

In the next chapter, you will learn how to integrate embedded analytics with your existing data model. This is an interesting chapter so see you.

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