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The art and science of determining where one table should leave off and another pick up is called normalization, which we'll cover in a future installment. One table generally, but not always, groups aspects of a single concept: a person's name and password, a hotel reservation's check-in and check-out dates, or a shipment's source, destination, status, and tracking number. And while databases go to considerable lengths to handle physical storage unsupervised, understanding how this process works and designing for it is especially important for "wide" tables with records containing many large values. Tables must be legible to users and developers, with their schemas making sense out of the information they represent. While it's hard to miss the mark by much in drawing up a list of each datum you're interested in and declaring this an integer, that a date, and so forth, effective relational database design depends first and foremost on well-thought-out and well-formulated table designs. The United States' most popular databases by state going into 2022Ĭalling tables the "basic building block" of relational databases is a little reductive.Traditional databases vs serverless databases.Introduction to common serverless challenges.Top 13 serverless computing and database providers.Introduction to database backup considerations.How microservices and monoliths impact the database.Syncing development databases between team members.Troubleshooting database outages and connection issues.What is connection pooling and how does it work?.Top 8 TypeScript ORMs, query builders, & database libraries: evaluating type safety.Top 11 Node.js ORMs, query builders & database libraries in 2022.Introduction to MongoDB database tools & utilities. ![]()
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