#️⃣Software Engineering: Mea Culpa (Part V: All-Knowing Developers are Back in Demand?)

Software Engineering Series

I’ve been reading many job descriptions lately related to my experience and curiously or not I observed that many organizations look for developers with Microsoft Dynamics experience in the CRM, respectively Finance and Operations (F&O) and Business Central (BC) areas. It’s a good sign that the adoption of Microsoft solutions for CRM and ERP increases, especially when one considers the progress made in the BI and AI areas with the introduction of Microsoft Fabric, which gives Microsoft a considerable boost. Conversely, it seems that the "developers are good for everything" syntagma is back, at least from what one reads in job descriptions. 

Of course, it’s useful to have an inhouse developer who can address all the aspects of an implementation, though that’s a lot to ask considering the different non-programming areas that need to be addressed. It’s true that a developer with experience can handle Requirements, Data and Process Management, respectively Data Migrations and Business Intelligence topics, though if one considers that each of the topics can easily become a full-time job before, during and post-project implementations. I’ve been there and I (hopefully) know that the jobs imply. Even if an experienced programmer can easily handle the different aspects, there will be also times when all the topics combined will be too much for a person!

It's not a novelty that job descriptions are treated like Christmas lists, but it’s difficult to differentiate between essential and nonessential skillset. I read many jobs descriptions lately in which among a huge list of demands, one of the requirements is to program in the F&O framework, sign that D365 programmers are in high demand. I worked for many years as programmer and Software Engineer, respectively in the BI area, where SQL and non-SQL code is needed. Even if I can understand the code in F&O, does it make sense to learn now to program in X++ and the whole framework? 

It's never too late to learn new tricks, respectively another programming language and/or framework. It even helps to provide better solutions in other areas, though frankly I would invest my time in other areas, and AI-related topics like AI prompting or Data Science seem to be more interesting in the long term, especially when they are already in demand!

There seems to be a tendency for Data Science professionals to do everything, building their own solutions, ignoring the experience accumulated respectively the data models built in BI and Data Analytics areas, as if the topics and data models are unrelated! It’s also true that AI-modeling comes with its own requirements in what concerns data modeling (e.g. translating non-numeric to numeric values), though I believe that common ground can be found!

Similarly, the notebook-based programming seems to replicate logic in each solution, which occasionally makes sense, though personally I wouldn’t recommend it as practice! The other day, I was looking at code developed in Python to mimic the joining of tables, when a view with the same could be easier (re)used, maintained, read and probably more efficient, even if different engines will be used. It will be interesting to see how the mix of spaghetti solutions will evolve over time. There are developers already complaining of the number of objects used in the process by building logic for each layer from the medallion architecture! Even if it makes sense from architectural considerations, it will become a nightmare in time.

One can wonder also about nomenclature used – Data Engineer or Prompt Engineering for the simple manipulation of data between structures in data transformations, respectively for structuring the prompts for AI. I believe that engineering involves more than this, no matter the context! 

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💎💫SQL Reloaded: Querying Azure Synapse Metadata for the D365 CRM & FO Tables Exported via Azure Synapse Link for Dataverse

Enabling Azure Synapse Link for Dataverse for Dynamics 365 CRM (D365 CRM) or for Dynamics 365 Finance & Operations (D365 FO) allows to have all the needed tables for reporting in a CDM structure, however the csv files with data don’t have any headers, which makes it challenging to use directly the files without the corresponding metadata. For example, attempting to define external tables would be useless without proper headers and data types. Fortunately, attribute’s name and data types are available in JSON files and can be queried. 

D365 CRM 

For CRM the files start with tables’ names and have the EntityMetadata.json postfix, the files being stored in the Microsoft.Athena.TrickleFeedService folder which lies with the other folders containing the data. Given that all files are in the same folder, the following query can be used to export the metadata for all or some of the tables. Just replace "(container name)" and "(data source)" with the corresponding values for your environment.

 

-- export definition for D365 CRM' CDM (all attributes, all tables)
SELECT DAT.EntityName
, DAT.AttributeName
, DAT.Timestamp
, DAT.AttributeType
, DAT.MetadataId
, DAT.Precision
, DAT.MaxLength
FROM openrowset(
        bulk '/(container name)/Microsoft.Athena.TrickleFeedService/*-EntityMetadata.json',
        data_source = '(data source)',
        format = 'csv',
        fieldterminator ='0x0b',
        fieldquote = '0x0b',
        rowterminator = '0x0b' --> You need to override rowterminator to read classic JSON
    ) with (doc nvarchar(max)) as rows
       CROSS APPLY OPENJSON(doc, '$.AttributeMetadata')--definitions[0].hasAttributes
       with (
         EntityName nvarchar(255) '$.EntityName'
        , AttributeName nvarchar(255) '$.AttributeName'
        , Timestamp nvarchar(50) '$.Timestamp'
        , AttributeType nvarchar(50) '$.AttributeType'
        , MetadataId nvarchar(100) '$.MetadataId'
        , Precision int '$.Precision'
        , MaxLength int '$.MaxLength'
     ) as DAT
WHERE DAT.EntityName IN ('lead', 'opportunity', 'product')

D365 FO 

For Finance & Operations folders’ structure is more complex, a whole hierarchy of folders being built based on a set of predefined categories. One would need to traverse the hierarchy structure to find the files. Thus, it might be easier to generate the metadata for each table. Files’ names start with tables’ names and have the .cdm.json postfix. The below query generates the metadata for the InventTable table. Just replace the "(container name)", "(instance name)" and "(data source)" with the values for your environment.

 

-- export definition for D365 FO's CDM (all attributes, specific table)

SELECT DAT.name
, DAT.dataFormat
FROM openrowset(
        bulk '/(container name)/(instance name)/Tables/SupplyChain/ProductInformationManagement/Main/InventTable.cdm.json',
        data_source = '(data source)',
        format = 'csv',
        fieldterminator ='0x0b',
        fieldquote = '0x0b',
        rowterminator = '0x0b' --> You need to override rowterminator to read classic JSON
    ) with (doc nvarchar(max)) as rows
       CROSS APPLY OPENJSON(doc, '$.definitions[0].hasAttributes')
       with (
          name nvarchar(255) '$.name'
        , dataFormat nvarchar(50) '$.dataFormat'
     ) as DAT

Further Steps

One can obtain thus the needed metadata, however after a first inspection, the data types are too general compared with the ones considered for the data source attributes. One can work probably with these data types as well, though there's a Microsoft recommendation to minimize the row length by using the smallest possible column size, which leads to better query performance. Exporting the metadata from the source system and matching the two datasets based on tables and attributes’ names would allow addressing this recommendation, even if this implies checking from time to time whether their definition changed. The trick is to keep the same sorting order like in the Synapse files. Unfortunately, not all SQL Server data types are supported (e.g. text, ntext, sql_variant, etc.) or are ideal to work with (e.g. money), however there are alternative data types that can be used.