> Imagine trying to upgrade a system with incredibly complex data on a BILLION users that has to work 24/7, then deal with massive data influx four times a year

> Imagine trying to upgrade a system with incredibly complex data on a BILLION users that has to work 24/7, then deal with massive data influx four times a year plus catastrophic data ingestion one time a year. I think you're over-estimating the amount of data involved. There were about 150M individual tax returns and ~5.8M corporate returns in 2015. Even if we assume 50 years of storage in the 'active' dataset that is < 8B returns. Also, most returns don't contain a particularly large amount of data in them. Pretty sure we're talking about an in-memory scale dataset here, certainly for the active set in any given year. It actually would not surprise me if we (current co) ingest more data in a week than the entirety of the US tax return history. > And not only take in and sort those records, but validate the data and perform calculations based on rules that change almost monthly, and that sometimes must be applied to years-old data and sometimes not. I might believe that is an issue if the IRS actually calculated everyones taxes for them and sent out a 'do you agree' form, but they don't. It's more like they randomly sample some returns based on some hand picked 'warning' flags and have someone manually check them, maybe with the help of some excel formulae.