PostgreSQL vs Python for data evaluation: what, why, and howRemoved
Blog post from Tiger Data
This blog post discusses the importance of evaluating data in the data analysis lifecycle, and provides an overview of how to use PostgreSQL and TimescaleDB to perform data evaluation tasks. The author highlights the benefits of using a relational database for data munging, including efficiency and intuitiveness. They provide examples of SQL queries that can be used to evaluate data, including sorting columns, displaying grouped data, finding abnormalities in the database, and creating histograms. The post also introduces various PostgreSQL and TimescaleDB functions, such as `time_bucket()`, `CTE's WITH AS`, `COUNT()`, `approx_percentile()`, `min_val()`, `max_val()`, `mean()`, `num_vals()`, `percentile_agg()` (aggregate), and `tdigest()` (aggregate). The author concludes by encouraging readers to try out PostgreSQL and TimescaleDB for their data evaluation tasks, and provides resources for getting started.
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