Have you sometimes wondered what kind of databases are used to record real-time data for example in aircraft black boxes?
Or what kind of databases are used in analysing and processing of data in real-time processing systems such as temperature monitoring systems at a processing plant.
If your answer to one or both of the questions was yes, then, this post will help you clear the dust.
Time series is basically data obtained from multiple sources like temperature, pressure or humidity sensors, over a specific time period. Sometimes this data needs to be analzed and processed in real time for decision making in intelligent systems.
It is no rocket science that traditional databases like SQL have done a tremendious job when it comes to retrieving and insertion but they become limited when processing time stamped data like tweets, or whatsapp posts. When the data becomes big the systems using relational databases tend to become slower. This is where time series databases fit in, to do a great job, as they can work flexibly to fetch data over long historical and real-time periods and they are also fitted for data that grows to zetabytes of memory.
Time series databases play a crucial role in data management because of their ability to fetch data collected over long periods to make analysis and forecasting, as done in weather forecasts , currency exchange rates and so on.
Artificial Intelligent systems (AI) leverages Time Series databases because of their strong processing abilities as compared to traditional database systems. Examples of time series databases include,Timescale, influxDB, Prometheus, Timestream and the list goes on.
If you have watched trends of anything, like the world cup trends below, forex trends, or similarly then you watched a visualization of time series data.
Visit Glowdom to find out some of the AI databases that are being harnessed to change lives through coding .
Source: Google-trends
