In truth, most raw time-series data doesn't change after it's been saved, so these complex aggregate calculations return the same results each time. Most developers head down one of these paths because we learn, often the hard way, that running reports and analytic queries over the same raw data, request after request, doesn't perform well under heavy load. And in all but a very few databases, all historical data is replaced each time, preventing developers from dropping older raw data to save space and computation resources every time the data is refreshed. Unfortunately, developers need to manage updates using TRIGGERs or CRON-like applications in all current implementations. While these VIEWS are flexible and easy to create, they are static snapshots of the aggregated data. Even today, development teams employ entire groups that specifically manage ETL processes for databases and applications because of the constant overhead of creating and maintaining the perfect process.
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