Imagine you have a massive amount of time-series data you want to explore and visualize. Seeing the latest trends, the historical patterns, and the outliers in your data can help you gain insights and make decisions. But how do you visualize and analyze time-series data effectively? How do you create graphs, plots, and other visualizations for real-time analytics showing the current state of your data and the historical changes over different time intervals? And how do you do it efficiently without sacrificing performance or accuracy?
In this article with Timescale we dive into practical examples of using materialized views and continuous aggregates to create fast, performant time-series graphs.
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Creating a Fast Time-Series Graph With Postgres Materialized Views