Stairway to Data, Level 5: Types of Scales – Part I
Joe Celko discusses Nominal, Categorical, Absolute, Ordinal and Rank scales. These are the weakest scales we can use, starting with the weakest.
Joe Celko discusses Nominal, Categorical, Absolute, Ordinal and Rank scales. These are the weakest scales we can use, starting with the weakest.
Joe Celko introduces more powerful scales such as Interval, Log interval and ratio scales; before moving on to conversions, punctuation and units. Finally he gives guidelines as to how best to use scales in a database.
Joe discusses how to deal with the kinds of encoding schemes, how to use them and how to design them. He discusses Enumeration, Measurement, Abbreviation and Algorithmic categories
Joe discusses Hierarchical, Vector and Concatenation encoding before rounding up with general guidelines for designing encoding schemes.
Before you start to think about your database schema or tables, you need to consider your data: the type of data it is, the scale you use for values. It needs to be unique, precise and unambiguous. Then you need to name it in such a way that it can be generally understood. Joe Celko explains...
A clear understanding of SQL Data Types and domains is a fundamental requirement for the Database Developer, but it is not elementary. If you select the most appropriate data type, it can sidestep a variety of errors. Furthermore, if you then define the data domains as exactly as possible via constraints, you can catch a variety of those problems that would otherwise bedevil the work of the application programmer.
There are several types of tables, each with their special requirements for rules and integrity constraints. Whatever the requirement, table-level constraints will ensure that the rules are enforced and data integrity is maintained.
Having described tables, Joe Celko explains how to make them work together as a database and touches on what Entity Relationships and Views are.
Joe Celko tackles the subject of the Stored Procedure and its place in database design. What he writes is food for thought, even for experienced database developers.
In levels one to four, we built the tables, base and virtual, of a schema. Levels five and six dealt with stored procedures. This level deals with a feature you need to avoid as much as possible; this is article is on Triggers.
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I have some data in a table that looks like this:
BeerID BeerName brewer beerdescription 1 Becks Interbrew Beck's is a German-style pilsner beer 2 Fat Tire New Belgium Toasty malt, gentle sweetness, flash of fresh hop bitterness. 3 Mac n Jacks Mac & Jack's Brewery This beer erupts with a floral, hoppy taste 4 Alaskan Amber Alaskan Brewing Alaskan Brewing Amber Ale is an "alt" style beer 8 Kirin Kirin Brewing Kirin Ichiban is a Lager-type beerIf I run this, what is returned?
select t1.[key]
from openjson((select t.* FROM Beer AS t for json path)) t1 See possible answers