"When Should You Use Normalization of Data—And When Should You Avoid It?"
Hey everyone! So, I’ve been diving into database design, and *normalization of data* keeps popping up. Like, it’s great for cutting redundancy and keeping things tidy, but is it *always* the way to go?
From what I get, normalization of data is a lifesaver for transactional systems where accuracy and consistency matter (think banking apps). But for analytics or reporting? Maybe not so much. Over-normalizing can make queries slower cuz of all the joins.
Also, ever worked with a denormalized setup? Sometimes it’s *way* faster for read-heavy stuff, even if it feels a bit messy.
So, when do you guys say "nah" to normalization of data? Or do you always stick to it? Curious to hear your takes!
(Also, sorry for any typos—typed this on my phone lol.)
Hey everyone! So, I’ve been diving into database design, and *normalization of data* keeps popping up. Like, it’s great for cutting redundancy and keeping things tidy, but is it *always* the way to go?
From what I get, normalization of data is a lifesaver for transactional systems where accuracy and consistency matter (think banking apps). But for analytics or reporting? Maybe not so much. Over-normalizing can make queries slower cuz of all the joins.
Also, ever worked with a denormalized setup? Sometimes it’s *way* faster for read-heavy stuff, even if it feels a bit messy.
So, when do you guys say "nah" to normalization of data? Or do you always stick to it? Curious to hear your takes!
(Also, sorry for any typos—typed this on my phone lol.)