At its core, a streaming database is one that is designed to collect, process and oftentimes even enrich a series of incoming data points – also commonly referred to as a data stream. It does this in real time, nearly instantly after the database is created.
It’s important to note that streaming databases are not themselves a particular class of database management systems. Instead, the term can be used to correctly refer to several different types of databases, so long as they all handle data in real time. This can include but is certainly not limited to things like in memory data grids, in memory databases, NewSQL and NoSQL databases, and time series databases.
What is a Streaming Database?
To really get a sense of how important a streaming database can be, you need to compare it to more traditional relational database management systems, otherwise known as RDBMSs. With an RDBMS, a database administrator would load data using a set of tools at regular intervals – this could be nightly, weekly or even more or less frequently depending on the situation. With a streaming database, this isn’t actually needed as by design it will collect incoming data in real time so that it can be immediately processed.
Why Use a Streaming Database?
All told, building a streaming database brings with it a wide range of different benefits that are particularly important in the fast-paced modern era we’re living in. Not only do they give developers a chance to respond to events faster than ever before (and certainly faster than a lot of their competitors who are still using RDBMSs), but it also enables real time alerting for changing conditions as well.
Likewise, streaming databases are great for supporting use cases where preventative maintenance is of paramount importance. Thanks to the fact that streaming databases allow you to realize data in real time as it is generated, they can also be a viable way to deploy real time machine learning interfaces as well.
How to Use a Streaming Database
You can use a streaming database in a wide range of different ways, including by storing data that itself can then be used to enrich your stream moving forward. Joining data obtained from sources like the Internet of Things with data from your streaming database can always provide more context for the purposes of analysis, which itself lets you make better and more informed decisions moving forward.
The rise of streaming database use has also grown in large part thanks to how they can move data from one source to another in real time, making them perfect for use with microservices architectures. They can serve as the foundation for sharing data between microservices and even for communication – something that will only become more important as time goes on.
Finally, streaming databases are also perfect for use with stream processing. Keep in mind that a lot of the data that we’re creating every day by way of various applications and machines is generated as a series of events that are playing out in an ongoing capacity. Because of the way they are set up, streaming databases can execute continuous queries that will then process these events as they occur, rather than as idle batches that are allowed to grow stale over time.
Streaming Database Best Practices
By far, one of the most important best practices to follow in terms of deploying your own streaming database involves building out the engine first. Once you build a core database engine, you can then work to stabilize that engine over a longer period of testing, fixing bugs and various iterations. Then, you can build a database management system that can then be scaled out as needed.
Generally speaking, you’ll want to build a single node streaming database to make sure that you have a rock solid foundation from which to scale from moving forward. By focusing your immediate efforts on building a highly performant single node database, you’ll put yourself in a better position to outperform competitors who are still working with medium sized clusters. Plus, you get the added benefit of a system that is so scalable that you won’t actually need multiple nodes, this despite the fact that multi node deployment is supported.