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Beyond NoSQL Database: Why AI Is Needed within NoSQL for Modern Use Cases

As technology progresses, artificial intelligence becomes necessary within NoSQL databases for modern use cases. Our AI database solution.

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Beyond NoSQL Database: Why AI Is Needed within NoSQL for Modern Use Cases
Those familiar with traditional NoSQL databases know that scalability, flexibility, and speed are primary concerns. Buy Mobile Database More data retrieved faster leads to actionable insights for developers and a better end-user experience.

Despite their growing popularity, NoSQL databases are challenged in four core competencies that limit their performance and function. afgsdfgsdf sasasas vfvfv

NoSQL Database Core Competency Limitations:

Complexity Limitations
Scalability Limitations
Rigidity Limitations
Cost Limitations
As long as these limitations continue, NoSQL databases cannot achieve their full performance capacity. Unfortunately, as an emerging technology, few solutions exist to overcome these problems today.

It is for this reason that artificial intelligence within NoSQL databases is needed for modern use cases. Best Database Provider More on that in a moment.

First, let’s look at how NoSQL databases work together with artificial intelligence for modern use cases right now. This will give you a broader picture of how AI convergence changes everything.

How Do NoSQL Databases Work with AI Right Now for Modern Use Cases?

NoSQL Databases offer numerous advantages over SQL databases. They’re more flexible with the types of data they can handle, more scalable in their ability to work with that data, and they speed up the process of data retrieval to produce valuable insights much faster than their counterpart.

Nonetheless, NoSQL databases are not without issues that limit their value for modern use cases.

NOSQL DATABASES ARE TOO COMPLEX
Currently, one of the chief complaints is complexity. Simply put, Buy Mobile Database traditional NoSQL databases require integration across too many silos, software stitching that takes months to complete, and they cannot directly call analytics from within the database itself for actionable real-time insights. This complexity slows the entire system and decreases its functionality for use cases.

NOSQL DATABASES HAVE LIMITED SCALABILITY
With so many silos required for integration, scalability caps out quickly. Since scaling each silo is a dynamic effort, the speed at which scaling can occur is limited. To make matters worse, the data produced between silos can be confusing and sometimes mismatched due to the number of silos involved. These limitations can make it nearly impossible to scale your app’s functionality to the highest levels.

NOSQL DATABASES ARE TOO RIGID
The rigidity of traditional NoSQL databases further slows their functionality. An analytic layer must be added since it is not already converged, and this means data is not streamed in real-time. In addition, coding is necessary for every new feature which can create a huge backlog cycle for feature additions and increase the time to market.

NOSQL DATABASES ARE TOO COSTLY

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Most traditional NoSQL databases require big iron appliances which can cost millions just to get started. Open-sourced development is, of course, an option; however, this is another Best Database Provider area that can significantly increase time to market. Yes, you could hire a team of data consultants to speed up the process, but again, this comes with enormous costs that eat into your bottom line.

Understanding the limitations of traditional NoSQL databases, let’s talk solutions.

Specifically, how do AI databases simplify the entire system while increasing scalability and flexibility and while significantly reducing costs?

What is an AI Database?
Perhaps the simplest place to start is by describing what an AI database is. An AI database is a NoSQL database that integrates artificial intelligence from within to eliminate the necessity of distributed silos, create a self-serving system that anyone can use, and to report real-time data at a fraction of the cost.

AI DATABASES ARE SIMPLE
Unlike traditional NoSQL databases, AI databases use software convergence to eliminate silos. This allows for an all-in-a-box, off-the-shelf model. In other words, you can have your database up and running in a couple of hours instead of months. The time difference alone can cut out tens of thousands of dollars in expenses.

AI DATABASES ARE SCALABLE
AI Database Solution
The elimination of silos and the convergence of AI within the database means there is no need to integrate heterogeneous items individually. Instead, an AI database uses a single distributed layer to free up resources and empower the database to rapidly scale where scaling was nearly impossible before.

AI DATABASES ARE SELF-SERVING
One of the biggest limitations of traditional NoSQL databases is the necessity of developers and coding. With AI databases, data is streamed in real-time which removes the need for an added analytic layer.

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