Should i normalize database

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All legal requirements for ecommerce that you must include in your shop. Create digital catalogues of furniture products that sell more from a single platform.

Get to know what catalog standards are most widely used in other Europe countries. Catalog Management Why is database normalization so important? Table of Contents What is database normalization? Objectives of database normalization Types of database normalization Database normalization phases Advantages of database normalization Conclusion: Should you normalize your databases? But are there any special rules for achieving this? Let's see if that's what your company might need.

To avoid creating and updating any unwanted data connections and dependencies. To prevent unwanted deletions of data. To optimize storage space. To reduce the delay and complexity of checking databases when new types of data need to be introduced. The concern about time-costly joins are often based on experience with poor designs.

In other words: good things happen. Where did you get the idea that "joins and foreign key constraints, etc. It's a very vague statement, and usually IMO there is no performance problems. Denormalisation is only rarely needed on an operational system. One system I did the data model for had tables or thereabouts at the time it was the largest J2EE system built in Australasia and had just 4 pieces of denormalised data.

Two of the items were denormalised search tables designed to facilitiate complex search screens one was a materialised view and the other two were added in response to specific performance requirements. Don't prematurely optimise a database with denormalised data.

That's a recipe for ongoing data integrity problems. Also, always use database triggers to manage the denormalised data - don't rely on the application do do it. Finally, if you need to improve reporting performance, consider building a data mart or other separate denormalised structure for reporting. Reports that combine requirements of a real-time view of aggregates calculated over large volumes of data are rare and tend to only occur in a handful of lines of business.

Systems that can do this tend to be quite fiddly to build and therefore expensive. You will almost certainly only have a small number of reports that genuinely need up-to-the minute data and they will almost always be operational reports like to-do-lists or exception reports that work on small amounts of data. Anything else can be pushed to the data mart, for which a nightly refresh is probably sufficient.

I'd say, yes. I've had to deal with badly structured DBs too many times to condone 'flat table' ones without a good deal of thought. Actually, inserts usually behave well on fully normalized DBs so if it is insert heavy this shouldn't be a factor. On an insert-heavy database, I'd definitely start with normalized tables. If you have performance problems with queries, I'd first try to optimize the query and add useful indexes.

Only if this does not help, you should try denormalized tables. Be sure to benchmark both inserts and queries before and after denormalization, since it's likely that you are slowing down your inserts. The general design approach for this issue is to first completely normalise your database to 3rd normal form, then denormalise as appropriate for performance and ease of access.

This approach tends to be the safest as you are making specific decision by design rather than not normalising by default.

The 'as appropriate' is the tricky bit that takes experience. Normalising is a fairly 'by-rote' procedure that can be taught, knowing where to denormalise is less precise and will depend upon the application usage and business rules and will consequently differ from application to application.

All your denormalisation decisions should be defensible to a fellow professional. For example if I have a one to many relations ship A to B I would in most circumstances leave this normalised, but if I know that the business only ever has, say, two occurrences of B for each A, this is highly unlikely to change, there is limited data in the B record.

Of course most passing DBA's will then immediately flag this up as a possible design issue, so you must be able to convincingly argue your justification for denormalisation. It should be apparent from this that denormalisation should be the exception. I don't know what you mean about creating a database by-the-book because most books I've read about databases include a topic about optimization which is the same thing as denormalizing the database design.

It's a balance act so don't optimize prematurely. The reason is that denormalized database design tend to be become difficult to work with. You'll need some metrics so do some stress-testing on the database in order to decide wether or not you wan't to denormalize. Stack Overflow for Teams — Collaborate and share knowledge with a private group. This in itself is actually two pieces of advice: i use surrogate keys in every table and ii use integers for surrogate keys.

Different people will give you different reasons for why they consider these best practices and all of these reasons can be argued over on a case by case basis. The best argument I can think of for using surrogate keys is that natural keys are more likely to be changed. Changing any primary key is a giant pain so it's best to avoid if you can. The best argument I can think of for using integers for your surrogate key is that it's a nice simple data type which is compact and efficient.

Again, this is highly situational so people will make an argument for or against this in different cases. What I would say about Recommendation 2 overall is this: pick a lane and stick to it so that your code is relatively consistent and diverge from this only when you have a really compelling case of critical performance or critical efficiency and you can clearly demonstrate that diverging from your usual approach has significant benefits.

In general, I avoid using natural keys, but there are times when you can get away with it, and even times when it makes better sense to use them. The question you need to ask yourself with a natural key is "Might this change in the future? My rule of thumb is "if a user can see it, they're going to want to change it someday". In your specific case though, language codes are set by an international standards body, so the chance that they might change are pretty slim — it would be too big of a pain.

I wouldn't hesitate in your case to use "en-us" as a key value. I'm going to answer your question by following a loose sense of the definition of database normalization I'm not looking to debate any keyboard warriors on the theoretical textbox definition , since that would be the best way to provide you with a reasonable answer to your practical question. So definitions aside, the root of your question is in practice, what are the benefits of refactoring a string-based column from one table into a separate table with a dedicated integer-based column?

Performance : I'm going to preface this by saying people will debate this til the cows come home because this is the least practical benefit, usually, but it is still an honest and important answer that there truly can be a performance difference under specific conditions. The second condition being that the string-based field typically stores larger values, e.

Thirdly, the size of the table, which will also affect the number of data pages that need to be loaded off disk, can compound the potential performance issues of the previous condition. If the right combination of the aforementioned conditions exist, using an integer-based field can improve performance, in practice. I've done so in even less extreme cases where replacing a UUID data type with an integer made a measurable difference in a few large tables.

It's certainly more of a micro-optimization, and your mileage may vary. As I mentioned at the start of this point, this is the least practical reason. Flexibility : I'm being a little lazy on this one, instead of re-typing what I've previously written in other answers on this point, here it is directly quoted link at the end of this answer :.

By having the fields of your data points broken out into appropriately less wide tables, that make general sense to your domain model, and keeping the closely related fields of a particular entity together in the same table, you maximize your ability to utilize, query, and manipulate those data points and entities as needed in your consuming applications. An example of this is if you had a Sales Order application that has two screens.

Maintainability : This is the most practical reason, in my opinion, on why refactoring a string-based column into its own table with a dedicated integer-based column is beneficial. Using your example dictionary table stores a language "code", imagine you filled it with 1 million records. Some of the language codes are "en-us", lets say half the table, about , records, for example. Then if a few months later, the business decides to shorten "en-us" to just "en". Well with this current single table design, it would require us to run an UPDATE statement that would need to modify , rows.

In a more refactored design when it makes sense , where there was a separate languageCodes table, you'd only store the value of "en-us" once and it's integer-based languageId column would be the key that is referenced , times in the dictionary table.

Now to make such a change, you would only need to UPDATE a single record in the languages table, and the language code would automatically be correct for all , records referencing it from the dictionary table. The improved maintainability does lend itself to potential performance improvements too. If your dictionary table is heavily used, an UPDATE to , rows that will lock that table for an measurable amount of time, might not be conducive to your goals.

You may benefit from this process as well. The same goes for those who work with database maintenance, ensuring everything is running smoothly on that front. In fact, pretty much anyone involved in data and analysis will find data normalization to be extremely useful. Data normalization should not be overlooked if you have a database, which goes for almost every business out there at this point. Sign up for a Free Trial today to use our SaaS tool to acquire web data yourself or talk to a data expert to see how Import.

What is data, and why is it important? How to get data from a website. May 7, Import. What is Data Normalization?

More Benefits of Data Normalization Simply being able to do data analysis more easily is reason enough for an organization to engage in data normalization. Recommended Reading What is data, and why is it important? How to get data from a website 8 fantastic examples of data storytelling. We use cookies to offer you a better browsing experience, analyze site traffic, personalize content, and serve targeted advertisements. If you continue to use this site, you consent to our use of cookies.