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Querying Relational Databases

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How Normalization Helps Us

2:54 with Andrew Chalkley and Kevin Nahm

Normalization eliminates the potential for update anomalies and the enforcement of data integrity. But what does that mean?

Review further explanations and-step-by step guides to normalization.

As with most IT topics, there are many books available on the subject. This is a good book for beginning database designers: Beginning Database Design Solutions, by Rod Stephens

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    After being introduced to the process of database normalization, 0:00
    you might be wondering why go to all that trouble? 0:04
    Think about a spreadsheet with millions of rows, maybe sales data. 0:08
    Each of those rows contain some sort of descriptive value, 0:12
    maybe it's the product name. 0:16
    All the storage space used to store the text of those product names over and 0:18
    over again can really add up. 0:22
    If you were to condense the list of product names down to a discrete 0:25
    distinct list and store it separately from the main dataset, 0:29
    you would drastically reduce the amount of disk storage space required. 0:34
    Instead of storing the product names over and 0:39
    over again, you can store the products IDs. 0:41
    Now, imagine that inefficiency repeated over and over again for 0:45
    the other columns in the spreadsheet. 0:49
    Such as the product category, store location and so on. 0:52
    Normalizing each of these lists of text becomes crucial to 0:55
    reducing disk space usage. 0:59
    In the 1970s and 80s when companies were fast investing in the technology to 1:01
    computerize their business systems, this forward thinking design 1:06
    approach literally saved millions of dollars in hardware costs. 1:10
    This storage cost have come down dramatically since then, but 1:15
    we're storing more and more data than ever before. 1:18
    So normalization is still a requirement. 1:21
    Let's think about our original large spreadsheet again. 1:24
    We see all the products listed out along with the rest of the cells data, 1:28
    let's say one of the product name's changes. 1:33
    We need to change it. 1:36
    Look up all the other places would need to make a change too. 1:37
    We hope we'd better not miss one. 1:40
    What happens if someone queries this dataset before 1:42
    we are completed with our updates? 1:46
    They could possibly see two different spellings for the same product. 1:48
    The data is inconsistent which is never good. 1:52
    In our normalized state, we can just change it in one place, 1:55
    in the products table. 2:00
    Changing one row of a thousands is, 2:02
    as you can imagine, a more optimal database operation. 2:05
    The problem with poorly structured data that I just described 2:09
    is called an update anomaly. 2:13
    This is a huge plus to normalization, eliminating the potential for 2:15
    update anomalies and the enforcements of data integrity. 2:19
    It's also more efficient for an applications to make an update to one row 2:24
    in a table opposed to millions of rows. 2:28
    In the world of split second application response times 2:31
    that time difference is critical to provide a positive user experience. 2:35
    By now, you should be starting to see the importance of good database normalization. 2:40
    There is a lot of great information online regarding normalization, be sure to check 2:46
    the teacher's notes for some links and suggested reading to learn more. 2:50

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