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If you are working with time-series data, you need a way to be able to easily manipulate, query, and visualize that data. Often times, time-series databases contain a number of time-oriented functions that aren't found in traditional databases.
These functions are meant to provide two key benefits: improved ease of use for time-series analytics, and improved performance. In this case, I'm going to demonstrate with two TimescaleDB functions time_bucket()and time_bucket_gapfill().
In almost every industry, there is a digital transformation underway that is driven by IoT and Big Data. What’s important to recognize is that IoT isn’t about things; it’s about the data those things collect. Organizations rely on this data to provide better user experiences, to make smarter business decisions, and, ultimately, fuel their growth.
However, none of this is possible without a reliable database that is able to handle the massive amounts of data generated by IoT devices. Relational databases are known for being flexible, easy to work with, and mature. What they aren’t particularly known for is scale, which prompted the creation of NoSQL databases. As you may or may not already know, there are ways to overcome this disadvantage.