Streaming Database Use Cases

At its core, a streaming database is one that is intended to collect, process and even enrich an incoming data stream in real-time, often right after the data itself is created. Note that this is in contrast to a more traditional Relational Database Management System, also commonly referred to as an RDBMS for short. In that scenario, a database administrator would load data via a series of tools at a predetermined frequency depending on their needs. This can happen weekly or even nightly as the context demands it.

It's equally important to note that a streaming database is not actually a specific type of database management system. Instead, it's a term used to describe any type of database that is set up to handle streaming data in real-time. This can include but is certainly not limited to in-memory databases, time-series databases, in-memory data grids and others.

Getting Data Visualizations in Real-Time

By far, one of the biggest advantages that building a streaming database brings to the table is that it allows organizational leaders to get data visualizations in real-time, thus quickly delivering the insight they need to make better and more informed decisions moving forward. By being able to analyze this volume of information in real-time soon after it is created, businesses can use this insight to improve agility, eliminate waste from their operations, improve customer service and more.

But most critically, they can also use it to quickly take advantage of business opportunities soon after they reveal themselves, as opposed to being forced to watch them pass by. It can take a naturally reactive enterprise and turn it into a proactive one, which is often more than worth the investment for most people.

Setting Live Alerts & Notifications

Another major benefit of streaming databases comes by way of how this type of configuration makes it possible to enable real-time alerting for certain changes as they take place. You don't have to worry about discovering some important conditional change days or even weeks after it has happened. Because data is analyzed in real-time as it is generated, those who need to be alerted to certain events can do so automatically.

This type of agility allows even smaller businesses to respond to events far faster than their competitors, for example. In an overarching sense, it can also support important goals like preventative maintenance. If you have a better understanding of how your equipment is functioning, you can stop a small problem today before it becomes a much bigger one down the road.

At a bare minimum, you know how to prioritize maintenance and even your budget in a way that will have the biggest positive impact of your total cost of ownership for that equipment. Likewise, you can prevent the types of extended (not to mention frustrating) periods of downtime that can quickly add up to significant costs by way of lost revenue.

Likewise, streaming databases also allow organizations to deploy real-time machine learning interfaces to automate a lot of the time-consuming and menial tasks they typically have to contend with, thus freeing up as much of their time as possible to focus on those matters that truly demand their attention.

Streaming Applications and Processing

In terms of streaming applications and processing, it's important to understand that a lot of the data that your average enterprise is working with these days is really being generated as a series of ongoing events. With a streaming database, it is possible to execute continuous queries in a way so that these events are processed as they occur as opposed to after the fact.

Not only does this help derive as much insight from your data as possible, but it also helps prevent issues down the road like suddenly dealing with massive volumes of stale data. In a larger sense, this type of processing can also help many companies become event-driven to begin with – all while still offering a lot of the same benefits as a traditional database, like its ability to integrate with many of the languages you're already working with.

Microservices Architectures

Streaming databases are also ideal when working within the microservices architecture. This is where, instead of building an application as a monolith as you would have years ago, you instead develop things as a set of loosely coupled but collaborating services.

Streaming databases help here because they can easily move data from one service to another in real-time. This helps all the various services interact with each other and can even help with messaging within this architecture.

Conclusion

In the end, streaming databases help organizations respond to events faster, remain more competitive, reduce waste and enable real-time alerting for changes – all at the exact same time. Given these benefits, it's easy to see why they've become so popular with so many people in recent memory.