Suggestion for Matrix Table representaton in SQL Server Tables - sql

Would like to get some suggestions on how to represent the following Matrix in an SQL Table like representation. Thinking of a three table structure like the following
Catid,CatName
LevelId,LevelName
Catid,MinValue,Maxvalue,LevelId
Any Suggestions? The idea is to get information about the level of approval required based on the Amount.

Related

Bigquery request on multiple tables

I would like to request on several tables with the following format name :
project.dataset.fr_table_20221001
project.dataset.fr_table_20221002
...
project.dataset.fr_table_20221030
project.dataset.en_table_20221001
project.dataset.en_table_20221002
...
project.dataset.en_table_20221030
etc with multiple langages and other dates.
Do you know what is the good syntax? Thank you in advance

By request did you mean retrieving table names on a specific dataset?
Can you try this query:
SELECT
CONCAT(table_catalog,'.',table_schema,'.',table_name) as Result
FROM
cars.INFORMATION_SCHEMA.TABLES;
you can change cars to your dataset to view the list of tables from yours. Please see sample output:

Different results BigQuery and Tableau

Hello guys when i try to calculate median and average in my BigQuery and Tableau i get different results even though I am using same numbers and rows. Is there something I should know?
For example;
In BiQuery
select district, avg(sales) from table name
In Tableau
Using district as dimension and select average of sales from the maxcard drop down menu.
Surprisingly the output from the two are not the same.
Any one knows what might be a problem that I should know?
Thanks!

Hi matt_black just got the solution, because in Tableau i made a join with polygon data when this happens it duplicates the data in the Tableau hence different results.

Is it possible to use SQL to show the average of some values in one column and then in subsequent columns display the individual values?

I have a bunch of data and I want the output to display an average of all the data points but also the individual data points in subsequent columns. Ideally it would look something like this:
Compound | Subject | Avg datapoint | Datapoint Experiment 1 | Datapoint Exp 2 | ...
..........XYZ......|.....ABC....|............40...............|...............20..............................|...............60...............|......
..........TUV......|.....ABC....|............30...............|...............20..............................|...............40...............|......
..........TUV......|.....DEF....|............20...............|...............10..............................|...............30...............|......
One problem I'm running in to is that I get repetitive lines of information. Another is that I have some rows pulling in info that doesn't apply, such that some of the individual datapoints in, say, row 2 would have info from subject DEF when I only want it to have info from subject ABC.
I hope this makes sense! I'm currently using inner join with a ton of where qualifiers. I'm close but not quite there. Any help is appreciate and let me know if I can provide additional info to help you help me.

The SQL language has a very strict rule requiring you to know the exact number of columns for your result set in advance, before looking at any data in your tables.
Therefore, if this average is based off a known fixed number of columns, or if the number of potential columns is reasonably small, where you can manually setup placeholders, then this will be possible. The key search terms to learn how to do this is "conditional aggregation", where you may also need to join the table to itself for each field.
Otherwise, you will need to pivot and aggregate your data in your client code or reporting tool.

How to populate all possible combination of values in columns, using Spark/normal SQL

I have a scenario, where my original dataset looks like below
Data:
Country,Commodity,Year,Type,Amount
US,Vegetable,2010,Harvested,2.44
US,Vegetable,2010,Yield,15.8
US,Vegetable,2010,Production,6.48
US,Vegetable,2011,Harvested,6
US,Vegetable,2011,Yield,18
US,Vegetable,2011,Production,3
Argentina,Vegetable,2010,Harvested,15.2
Argentina,Vegetable,2010,Yield,40.5
Argentina,Vegetable,2010,Production,2.66
Argentina,Vegetable,2011,Harvested,15.2
Argentina,Vegetable,2011,Yield,40.5
Argentina,Vegetable,2011,Production,2.66
Bhutan,Vegetable,2010,Harvested,7
Bhutan,Vegetable,2010,Yield,35
Bhutan,Vegetable,2010,Production,5
Bhutan,Vegetable,2011,Harvested,2
Bhutan,Vegetable,2011,Yield,6
Bhutan,Vegetable,2011,Production,3
Image of the above csv:
Now there is a very small country lookup table which has all possible countries the source data can come with, listed. PFB:
I want to have the output data's number of columns always fixed (this is to ensure the reporting/visualization tool doesn't get dynamic number columns with every day's new source data ingestions depending on the varying distinct number of countries present).
So, I've to somehow join the source data with the country_lookup csv and populate all those columns with default value as F. Every country column would be binary with T or F being the possible values.
The original dataset from the above has to be converted into below:
Data (I've kept the Amount field unsolved for column Type having Derived Yield as is, rather than calculating them below for a better understanding and for you to match with the formulae):
Country,Commodity,Year,Type,Amount,US,Argentina,Bhutan,India,Nepal,Bangladesh
US,Vegetable,2010,Harvested,2.44,T,F,F,F,F,F
US,Vegetable,2010,Yield,15.8,T,F,F,F,F,F
US,Vegetable,2010,Production,6.48,T,F,F,F,F,F
US,Vegetable,2010,Derived Yield,(2.44+15.2)/(6.48+2.66),T,T,F,F,F,F
US,Vegetable,2010,Derived Yield,(2.44+7)/(6.48+5),T,F,T,F,F,F
US,Vegetable,2010,Derived Yield,(2.44+15.2+7)/(6.48+2.66+5),T,T,T,F,F,F
US,Vegetable,2011,Harvested,6,T,F,F,F,F,F
US,Vegetable,2011,Yield,18,T,F,F,F,F,F
US,Vegetable,2011,Production,3,T,F,F,F,F,F
US,Vegetable,2011,Derived Yield,(6+10)/(3+9),T,T,F,F,F,F
US,Vegetable,2011,Derived Yield,(6+2)/(3+3),T,F,T,F,F,F
US,Vegetable,2011,Derived Yield,(6+10+2)/(3+9+3),T,T,T,F,F,F
Argentina,Vegetable,2010,Harvested,15.2,F,T,F,F,F,F
Argentina,Vegetable,2010,Yield,40.5,F,T,F,F,F,F
Argentina,Vegetable,2010,Production,2.66,F,T,F,F,F,F
Argentina,Vegetable,2010,Derived Yield,(2.44+15.2)/(6.48+2.66),T,T,F,F,F,F
Argentina,Vegetable,2010,Derived Yield,(15.2+7)/(2.66+5),F,T,T,F,F,F
Argentina,Vegetable,2010,Derived Yield,(2.44+15.2+7)/(6.48+2.66+5),T,T,T,F,F,F
Argentina,Vegetable,2011,Harvested,10,F,T,F,F,F,F
Argentina,Vegetable,2011,Yield,90,F,T,F,F,F,F
Argentina,Vegetable,2011,Production,9,F,T,F,F,F,F
Argentina,Vegetable,2011,Derived Yield,(6+10)/(3+9),T,T,F,F,F,F
Argentina,Vegetable,2011,Derived Yield,(10+2)/(9+3),F,T,T,F,F,F
Argentina,Vegetable,2011,Derived Yield,(6+10+2)/(3+9+3),T,T,T,F,F,F
Bhutan,Vegetable,2010,Harvested,7,F,F,T,F,F,F
Bhutan,Vegetable,2010,Yield,35,F,F,T,F,F,F
Bhutan,Vegetable,2010,Production,5,F,F,T,F,F,F
Bhutan,Vegetable,2010,Derived Yield,(2.44+7)/(6.48+5),T,F,T,F,F,F
Bhutan,Vegetable,2010,Derived Yield,(15.2+7)/(2.66+5),F,T,T,F,F,F
Bhutan,Vegetable,2010,Derived Yield,(2.44+15.2+7)/(6.48+2.66+5),T,T,T,F,F,F
Bhutan,Vegetable,2011,Harvested,2,F,F,T,F,F,F
Bhutan,Vegetable,2011,Yield,6,F,F,T,F,F,F
Bhutan,Vegetable,2011,Production,3,F,F,T,F,F,F
Bhutan,Vegetable,2011,Derived Yield,(2.44+7)/(6.48+5),T,F,T,F,F,F
Bhutan,Vegetable,2011,Derived Yield,(10+2)/(9+3),F,T,T,F,F,F
Bhutan,Vegetable,2011,Derived Yield,(6+10+2)/(3+9+3),T,T,T,F,F,F
The image of the above expected output data for a structured look at it:
Part 1 -
Part 2 -
Formulae for populating Amount Field for Derived Type:
Derived Amount = Sum of Harvested of all countries with T (True) grouped by Year and Commodity columns divided by Sum of Production of all countries with T (True)grouped by Year and Commodity columns.
So, the target is to have a combination of all the countries from source and calculate the sum of respective Harvested and Production values which then has to be divided. The commodity can be more than one in the actual scenario for any given country, but that should not bother as the summation of amount happens on grouped commodity and year.
Note: The users in the frontend can select any combination of countries. The sole purpose of doing it in the backend rather than dynamically doing it in the frontend is because AWS QuickSight (our visualisation tool), even though can populate sum on selected column filters but doesn't yet support calculation on those derived summed fields. Hence, the entire calculation of all combination of countries has to be pre-populated (very naive approach) in order to make it available in report on dynamic users selection of countries.
Also if you've any better approach (than the above naive approach mentioned in note) to solve this problem, you are most welcome to guide me. I've also posted a question on the same problem without writing my expected approach for experts to show me the path on how we can solve this kind of a problem better than this naive approach. If you want to help solve it with some other technique, you're most welcome, here is the link to that question.
Any help shall be greatly acknowledged.

Tableau count values after a GROUP BY in SQL

I'm using Tableau to show some schools data.
My data structure gives a table that has all de school classes in the country. The thing is I need to count, for example, how many schools has Primary and Preschool (both).
A simplified version of my table should look like this:
In that table, if I want to know the number needed in the example, the result should be 1, because in only one school exists both Primary and Preschool.
I want to have a multiple filter in Tableau that gives me that information.
I was thinking in the SQL query that should be made and it needs a GROUP BY statement. An example of the consult is here in a fiddle: Database example query
In the SQL query I group by id all the schools that meet either one of the conditions inside de IN(...) and then count how many of them meet both (c=2).
Is there a way to do something like this in Tableau? Either using groups or sets, using advanced filters or programming a RAW SQL calculated fiel?
Thanks!
Dubafek
PS: I add a link to my question in Tableu's forum because you can download my testing workbook there: Tableu's forum question

I've solved the issue using LODs (specifically INCLUDE and EXCLUDE statements).
I created two calculated fields having the aggregation I needed:
Then I made a calculated field that leaves only the School IDs that matches the number of types they have (according with the filtering) with the number of types selected in the multiple filter (both of the fields shown above):
Finally, I used COUNTD([Condition]) to display the amounts of schools matching with at least the School types selected.
Hope this helps someone with similar issue.
PS: If someone wants the Workbook with the solution I've uploaded it in an answer in the Tableau Forum