![]() Your incoming data may look something like this: timestamp Let’s say you have 1,000 devices out in the wild collecting environmental data at various intervals. Read -> /qoibnYesQG- Timescale February 4, 2020 #TSDBTipTuesday: With SQL you can JOIN your #timeseries data with your relational data, metadata & more to answer complex queries. ![]() For example, you might want to combine data or rows from two or more tables based on a common field – and this is where JOIN comes in. If you already work with time-series data, you may frequently run complex queries that go beyond the standard SELECT and WHERE commands. Tip #1: JOIN time-series data with relational data In this post, we've rounded up a few of our top tips and reasons why we love SQL for time-series analysis, including technical resources and guidance to help you get up and running. For these reasons, and many more, we believe SQL is the best language for working with - and getting the most value from - your time-series data. It’s also easy for organizations to adopt and integrate with other tools. ![]() SQL is a widely known, well documented, and expressive querying language (and the 3rd most popular development language as of writing). Get 4 quick tips for using your SQL skills for time-series data analysis, complete with technical guidance and advice.
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