![]() ![]() ![]() Meaning you’ll want to take the normalized data optimized for your application’s transactions, as well as all the data dumps from your third-party apps and services (think all that hard-won data from your customer relations management software), and ETL it into columns or tables that make it easy to answer questions like, how many customers signed up last month vs the previous, or which part of the onboarding funnel saw the biggest dropoff? Ideally, though, you’re going to want to organize your data in such a way that anticipates the kinds of questions you’re going to ask. Data warehouses are also essentially read-only the only thing that should be writing to your data warehouse are ETLs. It’s a place intended to keep data for analysis, not the needs of your application or service. The gist here is that the data warehouse is distinct from your production database, even if that data warehouse is just a replica of, say, your PostgreSQL production database. It could be a scalable database with columnar storage optimized for queries that touch a lot of data, or it could be a room with some file cabinets. Data warehouseĪ data warehouse is just a structured place where you put the data you want to query. If you’re looking for advice on what to use to store your analytical data, check out Which data warehouse should you use?. We wrote this up because you’ll probably hear these terms thrown around, and wanted to give you some context around each. ![]() Sometimes they can refer to something specific, other times they can refer to something super abstract. They’re mushy marketing words with overloaded metaphors, so even experienced data people can have a hazy idea of what, exactly, they refer to. The thing about these standard data warehouse terms is that they’re not great. ![]()
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