SQL is the lingua franca for analytics. As data proliferates, we need to find new ways to store, explore, and analyze it. We believe SQL is the best l

How to Write Better Queries for Time-Series Data Analysis With Custom SQL Functions

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2022-06-23 14:30:06

SQL is the lingua franca for analytics. As data proliferates, we need to find new ways to store, explore, and analyze it. We believe SQL is the best language for data analysis. We’ve championed the benefits of SQL for several years, even when many were swapping it for custom domain-specific languages. Full SQL support was one of the key reasons we chose to build TimescaleDB on top of PostgreSQL, the most loved database among developers, rather than creating a custom query language. And we were right—SQL is making a comeback (although it never really went away) and has become the universal language for data analysis, with many NoSQL databases adding SQL interfaces to keep up.

In addition, most developers are familiar with SQL, along with most data scientists, data analysts, and other professionals who work with data. Whether you've taken classes at university, done an online course, or attended a boot camp, chances are that you probably have learned a bit of SQL along the way. So you and your fellow developers already know it, making it easier for teams to onboard new members and quickly extract value from the data. With a proprietary language, learning the language is in itself a barrier to using the data—you’ll have to ask another team to write the queries or rely on a separate data lake.

Time-series data is ubiquitous. At Timescale, our mission is to serve developers worldwide and enable them to build exceptional data-driven products that measure everything that matters: software applications, industrial equipment, financial markets, blockchain activity, user actions, consumer behavior, machine learning models, climate change, and more.

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