Data is the lifeblood of any IoT application. In other words, IoT companies rely on their data to improve operations, provide better user experiences, make smarter business decisions and ultimately fuel growth.
However, none of this will be possible without a reliable database able to handle the massive amounts of data generated by IoT devices. When building an IoT application, you will want a database that’s scalable, performant, easy to work with and that can grow with your business. For these reasons, developers often turn to time series databases (TSDBes), which are known for their performance and ability to scale for IoT workloads.
IoT data is time series data
IoT data is actually two different data sets collected together: time series data, or data from the things, and metadata, which describes those things.
In particular, time series data can pile up very quickly. For example, a single connected car will collect 4,000 GB of data per day, which generates an abundance of information that needs to be collected, stored and analyzed. This is because time series workloads generally track new data points as inserts, not updates.
As a result, “normal” databases are just not equipped to handle the volume of data IoT devices generate. That’s where time series databases come in.
Time series databases are built for scale and speed
Not only are time series data workloads high volume, but they are also complex in nature — for example, powering a real-time operational dashboard or alerting system. This means that your IoT database needs to both scale and answer complex queries efficiently.
TSDBes, which can be based on relational or NoSQL databases, handle scale by introducing efficiencies that are only possible when you treat time as a first-class citizen. These efficiencies result in performance improvements, including higher ingest rates, faster queries at scale and better data compression. TSDBes also introduce features that aid with time series data manipulation, such as data retention policies, automatic aggregations, interpolation and so forth. The right TSDB will also have the ability to scale with your organization, from the cloud to the edge, which will significantly simplify your data infrastructure operational stack.
Optimize for time series data
If you are working with a specific type of data, it only makes sense to use a database that’s optimized for that workload. And TSDBes are specifically designed for IoT.
Additionally, if you want to avoid creating data silos, you should opt for a TSDB that will allow you to query time series, metadata, geospatial data and external data together at the database layer, which can greatly simplify your application layer. In general, this makes it easier to get proofs of concept and prototypes off the ground.
By using a time series database, IoT organizations can use the insights hidden in machine-generated data to build new features, automate processes and drive efficiency. So, if you are building a new IoT application or modernizing your existing data infrastructure, you should seriously consider using a time series database. Your future self will thank you!
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