A data validation test is performed so that analyst can get insight into the scope or nature of data conflicts. Data validation is a general term and can be performed on any type of data, however, including data within a single application such as Microsoft Excel or when merging simple data within a single data store. Ensure consistently high data quality across cloud and on-premises data sources.
This method of validation is very straightforward since these programs have been developed to understand your rules and the file structures you are working with. The ideal tool is one that lets you build validation into every step of your workflow, without requiring an in-depth understanding of the underlying format.
Software like FME enables you to customize data validation workflows precisely for your needs. You can create workflows that are specific to data validation, or add data validation as a step within other data integration workflows. Additionally, you can automatically run any data validation workflow on a schedule or on-demand which means you can build a workflow once, and reuse it over and over. FME supports over formats and applications through tools called readers and writers.
Each reader and writer has been designed to understand the specific nature of its data format to aid in the validation process. Readers and writers go beyond just understanding a file extension.
They understand based on function, too. For example, not all. This information will help you retrace your steps and reconfigure your workflow to fix the data. With FME you can ensure that data is correct contains no inconsistencies or errors , complete there are no missing fields where a value is required , and compliant meets the specifications of data model standards.
FME is recognized as the data integration platform with the best support for spatial data worldwide. However, it can handle much more than just spatial data. Be wary when using the numeric data type.
Some data types can carry out an extra type check. For example, a date data type will ensure that a date inputted existed at some point, or could exist in the future.
Presence — This is sometimes called Allow Blank or Mandatory. This type of validation compels the user to enter data in the required field. For example, in an address book, you can make either the address or phone number optional, while you must make the name field required. Leaving a mandatory field blank will trigger an error message that will prevent you from proceeding to the next step. Then I had a friend who had Heterochromia iridium two eyes of different colors that really messed up the Human Resources department.
Now going to go on to the second phase of validation: is the format in which it is presented in the database correct? SQL has a lot of advantages over filesystems. Strongly typed languages guarantee that at least you must pick an appropriate data type. SQL goes one step further by allowing you to put constraints on the declarations so that you can get a proper domain for a data element. The various data types available to you in SQL will require slightly different tricks to give validations.
A numeric value represents quantity, magnitude, position, sequence or a unit on a scale. It is not an identifier. It is not a string. You can do arithmetic on a numeric value, but it makes no sense to do such operations on an identifier or string. Would you take the square root of your credit card number and expect to get any meaningful data out of it? How about the third position in a waiting line divided by the seventh position in a waiting line?
Decimal numbers need to be declared within the precision and scale needed to represent your data accurately. That means I might be doing work in which it is perfectly fine for me to represent Pi as 3.
Validation of computations is another issue in itself. For now, I just want to deal with the raw data. Frankly, I would prefer not ever to use floating-point math again.
However, the admonition about not allowing negative numbers in floating-point column is also worth considering. The concept of reasonableness must have a context. In fact,that might even be a little low for them, given the number of banana splits their stores make in a given day. However, I can say with absolute certainty that I will never buy or consume bananas in my life. The most common validation failure is with integer data types. Look over your schema and you will see that most of the time these columns cannot be negative or must at least be one.
Such a constraint also passes information to the optimizer and could improve your query performance. This has two major advantages: it guarantees that you will not get an unwanted negative value in the data field and it lets a compiler pick a different internal representation which might have some advantages.
SQL is the first language to have temporal data types. Validating a data type should not be a problem unless for some reason you try to represent it as character strings or numerics. Unfortunately, SQL Server has settings that allow you to display dates in the local dialect.
The historical reasons for this have to do the fact that, at one point, COBOL was the only language that was used for commercial use and the only one that anyone really used to any serious work with dates. And you had to write your own routines for handling date arithmetic. Data validation comes in two forms: internal to the database and external to the real world.
An internal validation consists of checking to see that date values for an entity are in the correct temporal sequence. External validation checks a date against the real world.
At another level, did you show transactions on holidays when you were not open or show a date outside of a legally required waiting period? A simple ordinal date column in your calendar table can make some very complicated temporal computations much, much easier.
The simple situation is where the allowed string values are drawn from a limited set. The general rule of thumb is you use the IN predicate when the list of valid values is short whatever short means these days and relatively static. For example, a list of the two letter state codes would meet these criteria, since there are less than 60 of them 50 states, possessions and territories, and military postal addresses.
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