So far in our datastores series we have discussed SQL and NoSQL databases, object-oriented and edge databases, log stores, time series and flat file storage, and search appliances. In this last installment of the series, we’re going to touch on geospatial datastores.
Geospatial datastores are one of two things: Either they are physically built specifically to look at data on maps and where data lives, or they sub-specialize in different forms of data storage like SQL and NoSQL. These datastores basically give reference points to where the data is located. So, if you’re literally just looking at raw latitude and longitude data, storing it that way as the key relationship can be very useful. But if you’re loading a lot of data where you want to look at other relationships, then you go with the sub-specialized store and servers that deal with geospatial data.
Depending on what your use case is and what you are doing determines which type of geospatial store you might use. If you’re just putting data on a map and not worrying about relationships between it or trying to manipulate it, then you want to use a store that’s natively built for geospatial data rather than a traditional datastore.
This is all about specialization, which is a problem across computing in general. Many people in computing learn one or two languages and that’s it. And maybe those languages are flexible enough to do several tasks, but that doesn’t mean it’s efficient at completing those tasks. There are more than a handful of tools in your toolbox to work with, but some programmers have a hammer and think that everything looks like a nail when there are other tools that might be better and completing a task, like screwdrivers, wrenches, drills, pliers, etc. There are resources out there which might be a better fit for what you are trying to do, even if you can fit your square peg into a round hole, it’s not a good and tight fit.
Geospatial datastores are usually best utilized in conjunction with other datastores. Most programmers want to fit every piece of data into whatever database type they know how to use and are most familiar with, but the truth is that there are a variety of ways to manage data storage. Especially if you have a lot of data, utilizing multiple methods gives you more flexibility to efficiently manipulate and locate the information you need.
For example, you have a database where you store the bulk of your data. Maybe it’s SQL or maybe it’s NoSQL or some other type. You can then attach a log store to log events, you can attach a search appliance to quickly index and locate specific data points, you can attach a cache to store ephemeral data that you want to pull quickly and you can attach a geospatial datastore to see exactly where that data relates on a map.
Not all businesses are going to need every type of datastore or database. If you don’t have a lot of data or dont need to work with and manipulate it in different ways, your business may not need all of that. But it is possible to have a data storage system that utilizes multiple datastores for more flexibility and ease of use. If you are unsure what best suits your business, consult an expert, or hire someone to help you determine what is most efficient.