Random Denodo notes

In Denodo, the terms "view" and "source" refer to different components of the data virtualization process:

Source:

Definition: A source in Denodo is an external data repository from which data is retrieved. This could be a database, a file, a web service, or another external system.
Purpose: The source is the origin of the data. It provides the raw data that Denodo connects to and retrieves.
Example: An Oracle database, a CSV file, an API, etc.

View:

Definition: A view in Denodo is a virtual representation of data that has been retrieved and potentially transformed from one or more sources. It behaves similarly to a database view but exists virtually in Denodo.
Purpose: Views are used to model, transform, and combine data from various sources. They allow users to define the structure and format of the data they want to work with, applying transformations, joins, filters, and other operations.
Types of Views:
- Base View: Directly created from a data source, representing the raw data as it is.
- Derived View: Created from other views or a combination of views and can include transformations, joins, and other operations.

Key Differences:

Nature: A source is the actual location of the data, while a view is a virtual representation of that data within Denodo.
Function: Sources provide data, and views model and transform that data for user consumption.
Usage: You use a source to access the raw data, and you use a view to manipulate and present that data in a meaningful way.

In summary, the source is where the data originates, and the view is how that data is presented and manipulated in Denodo.

You said:

what does a p_ prefix typically mean in a denodo environment? For example, p_slate_person

ChatGPT said:

In a Denodo environment, the p_ prefix typically stands for "parameterized" or "procedure", depending on the context. Here's what it usually signifies:

1. Parameterized View:

Meaning: The p_ prefix often indicates that the view is parameterized, meaning it accepts input parameters that can be used to filter or customize the data returned by the view.
Example: p_slate_person could be a parameterized view where slate_person is filtered or customized based on input parameters such as a specific slatepersonrefid or createddate.
Usage: Parameterized views are useful for creating flexible queries that can be reused with different inputs.

2. Procedure (Less Common):

Meaning: In some cases, p_ could indicate a stored procedure or a procedure-like operation in Denodo. However, Denodo primarily focuses on views, and procedures are less commonly used compared to traditional databases.
Example: If p_slate_person were a procedure, it might perform a series of operations or transformations on the slate_person data.

Common Use Case for `p_` Prefix:

Filtering Data: A view like p_slate_person might allow users to pass parameters such as person_id or date_range to retrieve only relevant records.
Dynamic Data Retrieval: It provides a way to dynamically alter the query behavior without hardcoding specific values into the view.

Example of Parameterized View in Denodo:

sql

Copy code

SELECT * FROM slate_person WHERE slatepersonrefid = @slatepersonrefid

In this example, @slatepersonrefid is a parameter, and the view would be called p_slate_person.

In our use case, p_slate_person is where the null values are set on all of those empty fields we send; also, there is some sort of magic with the race access dates:

Matt vs ChatGPT

I asked ChatGPT:
in a union in denodo, what does this association mean? (bv_slate_tuv_person.slatepersonrefid = bv_slate_tuh_person.slatepersonrefid AND bv_slate_tuv_person.slatepersonrefid = bv_slate_tuf_person.slatepersonrefid AND bv_slate_tuv_person.slatepersonrefid = bv_slate_tup_person.slatepersonrefid)
ChatGPT said:
In Denodo, the expression you've provided appears to be part of a union operation where multiple base views are combined, and specific associations between fields are being established. Let's break down what this association means:

Components of the Expression:
bv_slate_tuv_person: A base view in Denodo.
slatepersonrefid: A field (likely a unique identifier or a key) in the bv_slate_tuv_person view.
Other Views:
bv_slate_tuh_person
bv_slate_tuf_person
bv_slate_tup_person
Each of these views also has a slatepersonrefid field.

Meaning of the Association:
Equality Conditions: The expression is asserting that the slatepersonrefid field in the bv_slate_tuv_person view must be equal to the slatepersonrefid fields in each of the other views (bv_slate_tuh_person, bv_slate_tuf_person, bv_slate_tup_person).

This could be part of:

Union Operation: Ensuring that records being combined across these views are related by the same slatepersonrefid.
Data Integrity: Making sure that the slatepersonrefid is consistent across these views, which might be necessary for joining or combining the data meaningfully.
Implications:
Data Consistency: This ensures that all the views are aligned based on the slatepersonrefid, likely representing the same entity (e.g., a person) across different datasets.
Union Specificity: The union will only combine rows where the slatepersonrefid matches across all the views, which is important for maintaining data integrity and correctness.
Example Use Case:
You might have different views representing different aspects of a person's data (like address, job, etc.). By associating them through the slatepersonrefid, you ensure that all information combined in the union corresponds to the same individual.
In summary, this association is used to ensure that the slatepersonrefid field consistently matches across the views involved in the union, thereby maintaining the integrity of the combined dataset.

You said:
if the denodo association contains the following expressions: (bv_slate_tuv_person.slatepersonrefid = bv_slate_tuh_person.slatepersonrefid AND bv_slate_tuv_person.slatepersonrefid = bv_slate_tuf_person.slatepersonrefid AND bv_slate_tuv_person.slatepersonrefid = bv_slate_tup_person.slatepersonrefid)
(bv_slate_tuv_person.createddate = bv_slate_tuh_person.createddate AND bv_slate_tuv_person.createddate = bv_slate_tuf_person.createddate AND bv_slate_tuv_person.createddate = bv_slate_tup_person.createddate) , does that mean only rows that have identical values across both slatepersonrefid and createddate?

ChatGPT said:

Yes, the expressions provided indicate that only rows with identical values across both slatepersonrefid and createddate fields will be considered in the association. Here's a breakdown of how this works:

Expressions Recap:
Slatepersonrefid Matching:
bv_slate_tuv_person.slatepersonrefid = bv_slate_tuh_person.slatepersonrefid
AND bv_slate_tuv_person.slatepersonrefid = bv_slate_tuf_person.slatepersonrefid
AND bv_slate_tuv_person.slatepersonrefid = bv_slate_tup_person.slatepersonrefid

Createddate Matching:
bv_slate_tuv_person.createddate = bv_slate_tuh_person.createddate
AND bv_slate_tuv_person.createddate = bv_slate_tuf_person.createddate
AND bv_slate_tuv_person.createddate = bv_slate_tup_person.createddate
Interpretation:
slatepersonrefid Matching: Ensures that the slatepersonrefid field (likely a unique identifier) in bv_slate_tuv_person matches the slatepersonrefid field in all other views (bv_slate_tuh_person, bv_slate_tuf_person, bv_slate_tup_person).

createddate Matching: Ensures that the createddate field (likely a timestamp or date of creation) in bv_slate_tuv_person matches the createddate field in all the other views.

Combined Effect:
Both Conditions Must Be True: For any row to be included in the result set, both the slatepersonrefid and createddate fields must match across all the views. This means:
If any of these fields have differing values in any of the views, those rows will be excluded from the result.
Only rows with matching slatepersonrefid and createddate across all views will be included.
Result:

In summary, yes, the provided association expressions ensure that only rows with identical values in both slatepersonrefid and createddate across all specified views will be included in the result.

With Matt in Teams:

(bv_slate_tuv_person.slatepersonrefid = bv_slate_tuh_person.slatepersonrefid AND bv_slate_tuv_person.slatepersonrefid = bv_slate_tuf_person.slatepersonrefid AND bv_slate_tuv_person.slatepersonrefid = bv_slate_tup_person.slatepersonrefid)

This is the first association in the u_slate_person union. I understand that the union is a combination of the base view tables for tuv, tuh, tuf, and tup, returning one row for each distinct slatepersonrefid.

We expect that to return one row for each person in each database, and will be very sad if there are ever any duplicates. Is this expression saying that the only condition under which any records in these four databases would be combined in the union is if there is a single record in all four databases with the exact same slatepersonrefid? If I have got that correct, since there are associations like that for each field, that the only way any merging records can happen is that if literally every single field was the same across all four base views?

No. The union being done in Denodo is a UNION ALL, which just joins the views together row-rise, there’s no special logic. If you have 6 views, 500 rows each, you end up with 3000 rows. I think that join you see is an abitrary column that Denodo picks that all views had in common to syntactically “join” the views together in VQL. But the contents of that column have no bearing on the output.

ohhh, so that wasn't something you built

Sort of. When you build things graphically Denodo generates VQL for you to represent what you built.

IDE = Integrated Development Environment; an IDE is a software application that increases developer productivity by combining capabilities such as software editing, building, testing, and packaging in an easy-to-use application. For Denodo, Design Studio is the IDE.