The history of Database management systems could be interpreted as a Darwinian evolution process.
The dominance of relational databases gives way to the data warehouses one, which better adapt to the earliest business intelligence requirements; then, alongside the rise of the most popular big data platforms such as Hadoop or spark, comes the era of the NoSQL databases, which were designed to privilege scalability among other features (instead of consistency for example).
However, what has happened during the years, is more likely a specialization process.
In fact, we are experiencing the coexistence of many DBMS paradigms, such as columnar, key-value, document, etc; therefore, instead of relying on a general-purpose unique standard, it is possible to choose the one that is better optimized to manage a particular kind of data and that better fits architectural and functional requirements, e.g. a relational paradigm is still one of the best choices in order to deal with ACID transactions, while a columnar storage fits perfectly in a data lake architecture and so on. Among the above-mentioned solutions, time-series databases are getting very popular during the last few years.
As the name suggests, a time-series database is designed and optimized to store data that evolves through time (time-series data). In other words, a time series is a (potentially unbounded) sequence of points for a particular metric over time. A metric is the measurement of a phenomenon that evolves through time.
For example, the number of people that come to a country for tourist purposes is an effective example of a metric.
This metric can have multiple dimensions (e.g. the origin country, the destination country) but it’s mandatory that every point has a timestamp and a numerical value that represents the measurement itself.