Abstract: Time series data is omnipresent in today’s data landscapes. Technical logging, financial transactions or IoT devices all generate data that is typically associated with a timestamp and can be categorized as time series data.
Even tough time series data stem from a plethora of sources, the structure of the data and the queries that are typically executed against this type of data are very similar throughout the different applications. Thus, it makes sense that a whole ecosystem of different databases evolved for working with time series data. The benefits of such specialized databases is that they are equipped with structures for supporting the different types of queries while at the same time providing techniques for the ingress of time series data as a stream of data.
One of the most popular specialized tools for working with time series data is the TimeScale extension for PostgreSQL databases. Installing and enabling this extension will result in the creation of indexes, triggers and procedures that facilitate working with time series data.
In our session, we will take a closer look at time series data and explain the different operations and data structures that can be utilized when working with time series data. We will discuss a strategy for benchmarking the efficiency of working with time series data. Next, we will take a closer look at the TimeScale extension for PostgreSQL and analyze its components. Finally, we will show which of these components can be implemented in an Azure SQL database and which need to be adjusted.
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