Some of the very obvious questions of course: Again, the number of table fields in a real world IoT temperature sensor would be higher, but our fields of interest in this case are restricted to timestamp of the temperature recording, device that reported it and the actual reading. The objective is to use PostgreSQL’s Window Functions for running analytics and that would not change with increasing the frequency of data points. For the sake of simplicity, however, I am going to use one reading per day.
In a typical scenario, temperature sensors are sending readings every minute.
#POSTGRESQL WINDOW FUNCTIONS SERIES#
Let’s take an example of IoT temperature sensor time series data spread over a period of 7 days. In particular, I am going to play around with PostgreSQL’s Window Functions here to analyze a sample of time series data from temperature sensors. Therefore, we get the power of complete set of analytic functions that PostgreSQL has to offer. Postgres-BDR is offered as an extension on top of PostgreSQL 10 and above. Let’s explore what the IoT Solution using Postgres-BDR has to offer for Data Analytics. find patterns, trends and make predictions. Often times this data is collected from geographically distributed sources and aggregated at a central location for data scientists to perform their magic i.e. Internet of Things tends to generate large volumes of data at a great velocity. PostgreSQL 9 Cookbook – Chinese Edition.PostgreSQL Server Programming Cookbook – 2nd Edition.PostgreSQL 9 Administration Cookbook – 3rd Edition.PostgreSQL High Availability Cookbook – 2nd Edition.
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