24 May 2020

Data Warehousing: SQL Server Integration Services (The Good, the Bad and the Ugly)

Data Warehousing

Microsoft SQL Server Integration Services (SSIS) is a platform for building (enterprise-level) data integrations and data transformations solutions by using a rich set of built-in tasks and transformations, graphical tools for building packages, respectively a catalog for storing the packages. Formally called Data Transformation Services (DTS), it was introduced with SQL Server 2000 and with SQL Server 2005 it was rebranded as SSIS.

The Good: Since its introduction it was adopted by DBAs and (database) programmers because it allowed the import and export of data on the fly from and to SQL Server, flat files, other relational data sources, in fact any resource exposing a driver for ODBC or OLEDB libraries. The extract/load functionality was extended by a basic set of transformations, making from DTS the ideal ETL tool for data warehousing and integrations. The data from multiple sources and targets could be processed in parallel or sequentially, the ETL logic being encapsulated in one or more packages that could be run manually or scheduled via the SQL Server agent flexibly.

With SQL Server 2005 and further versions the SSIS framework was extended to support further data sources including XML, CAML-based SharePoint lists, OData, Hadoop or Azure Bloob. It allowed the storage of packages on the local storage or within the built-in catalog.

One could thus develop rich ETL functionality without writing a single line of code. In theory the packages could be run and modified also by non-IT users, which can be a plus in certain scenarios. On the other side one could build custom packages programmatically from the beginning, and thus extend the available data processing logic as seemed fit, being able to using existing code and whole libraries embedded into the packages or run via dlls calls .

The Bad: Despite the rich functionality, a data pipeline usually has a lower performance and is more difficult to troubleshoot compared with the built-in RDBMS functionality for data processing. Most, if not all transformations can be handled over SQL-based queries more efficiently as long the data are available on the same SQL Server instance. In addition, SQL provides better code reuse, maintainability, chances for refactoring, scalability and the solutions are easier to deploy. Therefore, one practice resumes in using SSIS only for import/export, the further logic being encapsulated into stored procedures and further database objects. This isn’t necessarily bad, on contrary, though specific expertise is needed then to modify the code.

The Ugly: SSIS is in general suitable for data warehousing and integrations solutions whose logic is ideally stable and well-defined. Therefore, SSIS is less suitable for ERP data migrations or similar task which at least at the beginning have an exploratory nature and an overwhelming complexity, multiple iterations being needed before the requirements were fully identified and understood. In extremis each iteration can involve a redesign, which can prove to be time-consuming. One could in theory attempt first understanding all the data, though this could mean starting the development late in the process, while the data for testing are required much earlier. One can still use SSIS for specific tasks, though implementing a whole solution could imply certain challenges that otherwise could have been avoided.

SSIS is not suitable for real-time complex data integrations which require the processing of a considerable amount of data, when specific architectures like SOA, Restful calls or other solution could be more efficient. When not adequately implemented a data integration can lead to more problems than it can solve. Best example is the increase in execution time with the volume of data, fact that can easily lead to time-outs and locking of data.

Data Migrations: Migrating AdventureWorks to Dynamics 365 - Products

Below is exemplified the migration of Products from AdventureWorks database to Dynamics 365 (D365), where a minimum of steps were considered. Variations (e.g. enrichment of data, successive migrations) and other Entities (Product Variants, Released Products, Released Product Variants, etc.) will be considered in future posts.

As the AdventureWorks database is available only for testing and exemplification purposes, there is no need for a data import layer, the data being prepared into the “Map” schema created for this purpose. In theory the same approach can be used in production systems as well, though usually it’s better to detach the migration layer from the source system(s) from performance or security reasons.

-- creating a schema into the AdventureWorks database 
CREATE SCHEMA [Map]

Step 1: Data Discovery

Within this step one attempts getting a sorrow understanding of the systems involved within the data migration, in this case AdventureWorks and D365. As basis for this will be analyzed the tables for each entity, respectively the relations existing between them, the values, the distribution as well the relations existing between attributes. Is needed to analyze the similarities as well differences between the involved data models at structural as well at value level.

In AdventureWorks the SKU (Stock Keeping Unit) has a Color, Size and Style as Dimensions, a Product being created for each SKU. In D365 one differentiates between Products and Dimensions associated with it, having thus two levels defined. In addition, in D365 a Product has also the Configuration as dimension:


In addition, the Products and their Dimensions are defined at master level with a minimal of attributes like the Dimension Group. After that the Products and their Dimensions can be released for each Business Unit (aka Data Area), where the detailed attributes for Purchasing, Sales, Production or Inventory are maintained. For those acquainted with Dynamics AX 2009 it’s the same structure.
Once the structural differences identified, one can start looking at the values that define a product in both systems. The following queries are based on the source system.


-- reviewing the sizes 
SELECT Size 
, count(*) NoSizes
FROM [Production].[vProductDetails]
WHERE CultureId = 'en'
GROUP BY Size
ORDER BY Size

-- reviewing the colors 
SELECT Color
, count(*) NoColors
FROM [Production].[vProductDetails]
WHERE CultureId = 'en'
GROUP BY Color
ORDER BY Color

-- reviewing the styles  
SELECT Style
, count(*) NoStyles
FROM [Production].[vProductDetails]
WHERE CultureId = 'en'
GROUP BY Style
ORDER BY Style
If the above queries show what values are used, the following shows the dependencies between them:
-- reviewing the sizes, colors, styles  
SELECT Size
, Color
, Style
, count(*) NoValues
FROM [Production].[vProductDetails]
WHERE CultureId = 'en'
GROUP BY Size
, Color
, Style
ORDER BY 1,2,3

-- reviewing the dependencies between sizes, colors, styles  
SELECT CASE WHEN IsNull(Size, '') != '' THEN 'x' ELSE '' END HasSize
, CASE WHEN IsNull(Color, '') != '' THEN 'x' ELSE '' END HasColor
, CASE WHEN IsNull(Style, '') != '' THEN 'x' ELSE '' END HasStyle
, count(*) NoValues
FROM [Production].[vProductDetails]
WHERE CultureId = 'en'
GROUP BY CASE WHEN IsNull(Size, '') != '' THEN 'x' ELSE '' END 
, CASE WHEN IsNull(Color, '') != '' THEN 'x' ELSE '' END 
, CASE WHEN IsNull(Style, '') != '' THEN 'x' ELSE '' END
ORDER BY 1,2,3

The last query is probably the most important, as it shows how the products need to be configured into the target system:



As can be seen a product can have only Color, Color and Style, Size and Color, respectively no dimensions or all dimensions. It will be needed to define a Dimension Group for each of these cases (e.g. Col, ColSty, SizCol, SizColSty, None). (More information on this in a future post.)

Unfortunately, unless the target system is already in use, there are no values usually, though one can attempt entering a few representative values manually over the user interface, at least to see what tables get populated.

Step 2: Data Mapping

Once the main attributes from source and target were identified, one can create the mapping at attribute level between them. Typically, one includes all the relevant information for a migration, from table, attribute, description to attributes’ definition (e.g. type, length, precision, mandatory) in all the systems:


The mapping was kept to a minimum to display only the most relevant information. Except a warning concerning the length of an attribute, respectively a new attribute (the old item number), the mapping doesn’t involve any challenges.

A data dictionary or even a metadata repository for the involved systems can help in the process, otherwise one needs to access the information from the available documentation or system’s metadata and prepare the data manually.

The relevant metadata for D365 can be obtained from the Microsoft documentation. The data can be loaded into system via the EcoResProductV2Entity (see also data entities or the AX 2012 documentation for tables and enumeration data types).

Step 3: Building the source entity

AdventureWorks already provides a view which models the Products entity, though because of its structure it needs to suffer some changes, or sometimes more advisable, do the changes in a separate view as follows:
-- Products source entity 
CREATE VIEW Map.vProductDetails 
AS 
SELECT CASE WHEN Size<>'' THEN dbo.CutLeft(ProductNumber, '-',1) ELSE ProductNumber End ItemIdOld 
, CASE WHEN Size<>'' THEN dbo.CutLeft(Name, '-',1) ELSE Name End Name 
, row_number() OVER(PARTITION BY CASE WHEN Size<>'' THEN dbo.CutLeft(ProductNumber, '-',1) ELSE ProductNumber End ORDER BY ProductNumber) Ranking
, ProductNumber 
, Description 
, Color
, Size
, Style
, CultureId 
, Subcategory 
, Category
, MakeFlag
, FinishedGoodsFlag
, SellStartDate 
, SellEndDate 
, StandardCost 
, ListPrice 
, SafetyStockLevel 
, ReorderPoint 
FROM [Production].[vProductDetails]

-- reviewing the data 
SELECT *
FROM Map.vProductDetails 
WHERE CultureId = 'en'
ORDER BY ProductNumber


To prepare the data for the migration the Product Number as well the Name were stripped from the Size, this being done with the help of dbo.CutLeft function. The row_number ranking window function was used to allow later selecting the first size for a given Product.

The discovery process continues, this time in respect to the target. Its useful to understand for example whether a Product has more than one Color or Style, whether the prices vary between Sizes, whether attributes like the Subcategory are consistent between Sizes, etc. It’s useful to prove anything that could have impact on the migration logic. The list of tests will be extended while building the logic, as new information are discovered.

-- checking dimensions' definition
SELECT ItemidOld 
, count(Size) NoSizes
, count(DISTINCT Color) NoColors
, count(DISTINCT Style) NoStyles
FROM Map.vProductDetails 
WHERE CultureId = 'en'
  --AND Ranking = 1
GROUP BY ItemidOld
ORDER BY ItemidOld

-- checking the price variances between dimensions 
SELECT ItemidOld 
, Min(StandardCost) MinStandardCost
, Max(StandardCost) MaxStandardCost
FROM Map.vProductDetails 
WHERE CultureId = 'en'
  AND Ranking = 1
GROUP BY ItemidOld
HAVING Min(IsNull(StandardCost, 0)) != Max(IsNull(StandardCost, 0)) 
ORDER BY ItemidOld

-- checking attribute's consistency between dimensions 
SELECT ItemidOld 
, Min(Subcategory) MinSubcategory
, Max(Subcategory) MaxSubcategory
FROM Map.vProductDetails 
WHERE CultureId = 'en'
  AND Ranking = 1
GROUP BY ItemidOld
HAVING Min(IsNull(Subcategory, '')) != Max(IsNull(Subcategory, '')) 
ORDER BY ItemidOld

When the view starts performing poorly, for example because of the number of joins or data’s volume, it might me useful to dump the data in a table and perform the tests on it.
Even if it’s not maybe the case, it’s useful to apply defensive techniques in the logic by handing adequately the nulls.

Step 4: Implementing the Mapping 

The attributes which need to be considered here are based on the target entities. It might be needed to include also attributes that are further needed to build the logic.

-- Product Mapping 
CREATE VIEW [Map].vEcoResProductV2Entity
AS
SELECT ProductId 
, ItemidOld ItemId 
, 'Item' ProductType 
, 'ProductMaster' ProductSubtype 
, Left(Replace(Name, ' ', ''), 20) ProductSearchName 
, ItemidOld ProductNumber 
, Name ProductName 
, Description ProductDescription 
, CASE 
 WHEN IsNull(Size, '') !='' AND IsNull(Color, '') !='' AND IsNull(Style, '')!='' THEN 'SizColSty'
 WHEN IsNull(Size, '') !='' AND IsNull(Color, '') !=''  THEN 'SizCol'
        WHEN IsNull(Style, '') !='' AND IsNull(Color, '') !=''  THEN 'ColSty'
 WHEN IsNull(Color, '') !='' THEN 'Col'
 WHEN IsNull(Style, '')!='' THEN 'Sty'
        WHEN IsNull(Size, '')!='' THEN 'Siz'
 ELSE 'None'
  END ProductDimensionGroupName 
, 'WHS' StorageDimensionGroupName 
, CASE WHEN MakeFlag = 1 THEN 'SN' ELSE '' END TrackingDimensionGroupName 
, 'PredefinedVariants' VariantConfigurationTechnology 
, Subcategory ProductCategory 
, ItemidOld ItemIdOld 
, 1 IsNewItem 
FROM Map.vProductDetails 
WHERE CultureId = 'en'
  AND Ranking = 1

The Dimenstion Group is based on the above observation. It was supposed that all Products have inventory (see Storage Dimension), while the manufactured products will get a Serial Number (see Tracking Dimension). IsNewItem will be used further to migrate deltas (and thus to partition migrations).

Step 5: Building the Target Entity

The target entity is in the end only a table in which usually are kept only the attributes in scope. In this case the definition is given by the following DDL:

CREATE TABLE Map.EcoResProductV2Entity(
 Id int IDENTITY(1,1) NOT NULL,
 ProductId int NULL,
 ProductType nvarchar(20) NULL,
 ProductSubtype nvarchar(20) NULL,
 ProductsearchName nvarchar(255) NULL,
 ProductNumber nvarchar(20) NOT NULL,
 ProductName nvarchar(60) NULL,
 ProductDescription nvarchar(1000) NULL,
 ProductDimensionGroupName nvarchar(50) NULL,
 StorageDimensionGroupName nvarchar(50) NULL,
 TrackingDimensionGroupName nvarchar(50) NULL,
 VariantConfigurationTechnology nvarchar(50) NULL,
 ProductCategory nvarchar(255) NULL,
 ItemIdOld nvarchar(20) NULL,
 IsNewItem bit,
 CONSTRAINT I_EcoResProductV2Entity PRIMARY KEY CLUSTERED 
(
 Id ASC
)WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY]
) ON [PRIMARY] 

The table was build to match the name and definition from the target systems. The definition is followed by a few inserts based on the logic defined in the previous step:

-- preparing the data for EcoResProductV2Entity 
INSERT INTO [Map].EcoResProductV2Entity 
SELECT ITM.ProductId
, ITM.ProductType
, ITM.ProductSubtype
, ITM.ProductsearchName
, ITM.ProductNumber
, ITM.ProductName
, ITM.ProductDescription
, ITM.ProductDimensionGroupName 
, ITM.StorageDimensionGroupName 
, ITM.TrackingDimensionGroupName 
, ITM.VariantConfigurationTechnology
, ITM.ProductCategory
, ITM.ItemIdOld 
, ITM.IsNewItem
FROM [Map].VEcoResProductV2Entity ITM
WHERE ITM.IsnewItem = 1
ORDER BY ProductType 
, ITM.ItemIdOld 

-- reviewing the data 
SELECT *
FROM [Map].EcoResProductV2Entity 
ORDER BY ItemId

The business might decide to take over the Product Number into the target system as unique identifier, though it’s not always the case. It might opt to create a new sequence number, which could start e.g. with 10000000 (8 characters). In such a case is changed only the logic for the Product Number, the value being generated using a ranking window function:

-- preparing the data for EcoResProductV2Entity  
DECLARE @StartItemId as int = 10000000
--INSERT INTO [Map].EcoResProductV2Entity 
SELECT ...
, @StartItemId + Rank() OVER(ORDER BY ITM.ProductType, ITM.ItemIdOld) ProductNumber
, ...
FROM [Map].VEcoResProductV2Entity ITM
WHERE ITM.IsnewItem = 1
ORDER BY ProductType 
, ITM.ItemIdOld 

Step 5: Reviewing the Data

Before exporting the data it makes sense to review the data from various perspectives: how many Products of a certain type will be created, whether the current and old product numbers are unique, etc. The scripts make sure that the consistency of the data in respect to the future systems was achieved. 

-- checking values' frequency (overview, no implications)
SELECT ITM.ProductType
, ITM.ProductSubtype
, ITM.ProductDimensionGroupName 
, ITM.StorageDimensionGroupName 
, ITM.TrackingDimensionGroupName 
, ITM.VariantConfigurationTechnology
, count(*) NoRecords
FROM [Map].EcoResProductV2Entity ITM
WHERE IsNewItem = 1
GROUP BY ITM.ProductType
, ITM.ProductSubtype
, ITM.ProductDimensionGroupName 
, ITM.StorageDimensionGroupName 
, ITM.TrackingDimensionGroupName 
, ITM.VariantConfigurationTechnology

-- check ProductNumber's uniqueness (no duplicates allowed)
SELECT ProductNumber
, Min(ItemidOld) 
, max(ItemIdold)
, count(*)
FROM [Map].EcoResProductV2Entity
GROUP BY ProductNumber
HAVING count(*)>1

-- check old Product's uniqueness (no duplicates allowed)
SELECT ItemIdOld
, count(*)
FROM [Map].EcoResProductV2Entity
GROUP BY ItemIdOld
HAVING count(*)>1

This section will grow during the implementation, as further entities will be added.

Step 6: Exporting the data

The export query is usually reflecting the entity and can include further data’s formatting, when needed:

-- Export Products
SELECT ITM.ProductType
, ITM.ProductSubtype
, ITM.ProductsearchName
, ITM.ProductNumber
, ITM.ProductName
, ITM.ProductDescription
, ITM.ProductDimensionGroupName 
, ITM.StorageDimensionGroupName 
, ITM.TrackingDimensionGroupName 
, ITM.VariantConfigurationTechnology
, ITM.ProductCategory RetailProductCategoryName 
, ITM.ItemIdOld
FROM [Map].EcoResProductV2Entity ITM
WHERE ITM.isNewItem = 1
ORDER BY ProductNumber

Depending on the import needs, the data can be exported to Excel or a delimited text file (e.g. “|” pipe is an ideal delimiter.

Step 7: Validating the Data before Import

Before importing the data into the target system, it makes sense to have the data checked by the business or consultants. A visual check at this stage can help save time later.

Step 8: Validating the Data after Import

Unfortunately Microsoft doesn’t allow direct access to the D365 Production database, however one can still access various tables and entities’ content via the table browser. Anyway, the validation usually takes place into the UAT (User Acceptance Testing) system. So, if everything went well into the UAT and all measures were taken to have the same parameters across all systems, there should be no surprises during Go-Live.


21 May 2020

SQL Reloaded: Functions Useful in Data Migrations

Left and Right-Padding

Oracle has the LPAD and RPAD functions which return and expression, left-padded, respectively right-padded with the specified characters. The functions are useful in formatting unique identifiers to fit a certain format (eg. 0000012345 instead of 12345). Here’re similar implementations of the respective functions:

-- left padding function 
CREATE FUNCTION dbo.LeftPadding( 
  @str varchar(50) 
, @length int  
, @padchar varchar(1))
RETURNS varchar(50)
AS 
BEGIN 
  RETURN CASE  
    WHEN LEN(@str)<@length THEN Replicate(@padchar, @length-LEN(@str)) + @str       
    ELSE @str  
    END 
END  

-- example left padding 
SELECT dbo.LeftPadding('12345', '10', '0')

-- right padding function 
CREATE FUNCTION dbo.RightPadding( 
  @str varchar(50) 
, @length int  
, @padchar varchar(1))
RETURNS varchar(50)
AS 
BEGIN 
  RETURN CASE  
    WHEN LEN(@str)<@length THEN @str + Replicate(@padchar, @length-LEN(@str))      
	ELSE @str  
  END
END  

-- example right padding 
SELECT dbo.RightPadding('12345', '10', '0')

Left and Right Side

When multiple pieces of data are stored within same same attribute, it’s helpful to get the left, respectively the right part of the string based on a given delimiter, where the reverse flag tells the directions in which the left is applied:

-- left part function 
CREATE FUNCTION dbo.CutLeft( 
  @str varchar(max) 
, @delimiter varchar(1)
, @reverse bit = 0)
RETURNS varchar(max)
AS 
BEGIN 
  RETURN CASE  
     WHEN CharIndex(@delimiter, @str)>0 THEN 
       CASE @reverse 
         WHEN 0 THEN Left(@str, CharIndex(@delimiter, @str)-1)    
         ELSE Left(@str, Len(@str)-CharIndex(@delimiter, Reverse(@str))) 
       END
	 ELSE @str  
  END
END  

-- example left part 
SELECT dbo.CutLeft('12345,045,000', ',', 0)
, dbo.CutLeft('12345,045,000', ',', 1)



-- right part function 
CREATE FUNCTION dbo.CutRight( 
  @str varchar(max) 
, @delimiter varchar(1)
, @reverse bit = 0)
RETURNS varchar(max)
AS 
BEGIN 
  RETURN CASE  
    WHEN CharIndex(@delimiter, @str)>0 THEN 
      CASE @reverse 
        WHEN 0 THEN Right(@str, CharIndex(@delimiter, Reverse(@str))-1) 
        ELSE Right(@str, Len(@str)-CharIndex(@delimiter, @str)) 
      END
   ELSE @str  
  END
END  

-- example right part 
SELECT dbo.CutRight('12345,045,000', ',', 0)
, dbo.CutRight('12345,045,000', ',', 1)

Replacing Special Characters

Special characters can prove to be undesirable in certain scenarios (e.g. matching values, searching). Until a “Replace from” function will be made available, the solution is to include the replacements in a user-defined function similar with the one below:

DROP FUNCTION IF EXISTS [dbo].[ReplaceSpecialChars]

CREATE FUNCTION [dbo].[ReplaceSpecialChars](
@string nvarchar(max)
, @replacer as nvarchar(1) = '-'
) RETURNS nvarchar(max)
-- replaces special characters with a given character (e.g. an empty string, space)
AS
BEGIN   
  IF CharIndex('*', @string) > 0  
     SET @string = replace(@string, '*', @replacer)	
    
  IF CharIndex('#', @string) > 0  
     SET @string = replace(@string, '#', @replacer)	
    
  IF CharIndex('$', @string) > 0  
     SET @string = replace(@string, '$', @replacer)	
    
  IF CharIndex('%', @string) > 0  
     SET @string = replace(@string, '%', @replacer)	
    
  IF CharIndex('&', @string) > 0  
     SET @string = replace(@string, '&', @replacer)	
    
  IF CharIndex(';', @string) > 0  
     SET @string = replace(@string, ';', @replacer)	
    
  IF CharIndex('/', @string) > 0  
     SET @string = replace(@string, '/', @replacer)	
    
  IF CharIndex('?', @string) > 0  
     SET @string = replace(@string, '?', @replacer)	
    
  IF CharIndex('\', @string) > 0  
     SET @string = replace(@string, '\', @replacer)	
    
  IF CharIndex('(', @string) > 0  
     SET @string = replace(@string, '(', @replacer)	
    
  IF CharIndex(')', @string) > 0  
     SET @string = replace(@string, ')', @replacer)	
    
  IF CharIndex('|', @string) > 0  
     SET @string = replace(@string, '|', @replacer)	
    
  IF CharIndex('{', @string) > 0  
     SET @string = replace(@string, '{', @replacer)	
    
  IF CharIndex('}', @string) > 0  
     SET @string = replace(@string, '}', @replacer)	
    
  IF CharIndex('[', @string) > 0  
     SET @string = replace(@string, '[', @replacer)	
    
  IF CharIndex(']', @string) > 0  
     SET @string = replace(@string, ']', @replacer)	
                                
  RETURN (LTrim(RTrim(@string)))
END

SELECT [dbo].[ReplaceSpecialChars]('1*2#3$4%5&6;7/8?9\10(11)12|13{14}15[16]', '')
SELECT [dbo].[ReplaceSpecialChars]('1*2#3$4%5&6;7/8?9\10(11)12|13{14}15[16]', ' ')

Other type of special characters are the umlauts (e.g. ä, ß, ö, ü from German language):

DROP FUNCTION IF EXISTS [dbo].[ReplaceUmlauts]

CREATE FUNCTION [dbo].[ReplaceUmlauts](
@string nvarchar(max)
) RETURNS nvarchar(max)
-- replaces umlauts with their equivalent
AS
BEGIN   
  IF CharIndex('ä', @string) > 0  
     SET @string = replace(@string, 'ä', 'ae')	
    
  IF CharIndex('ö', @string) > 0  
     SET @string = replace(@string, 'ö', 'oe')	
    
  IF CharIndex('ß', @string) > 0  
     SET @string = replace(@string, 'ß', 'ss')	
    
  IF CharIndex('%', @string) > 0  
     SET @string = replace(@string, 'ü', 'ue')	
    
                                
  RETURN Trim(@string)
END

SELECT [dbo].[ReplaceUmlauts]('Hr Schrötter trinkt ein heißes Getränk')

Handling Umlauts

Another type of specials characters are the letter specific to certain languages that deviate from the Latin characters (e.g. umlauts in German, accents in French), the usage of such characters introducing further challenges in handling the characters, especially when converting the data between characters sets. A common scenario is the one in which umlauts in ISO-8891-1 are encoded using two character sets. Probably the easiest way to handle such characters is to write a function as follows:

-- create the function 
CREATE FUNCTION [dbo].[ReplaceCodes2Umlauts](
  @string nvarchar(max)
) RETURNS nvarchar(max)
-- replaces ISO 8859-1 characters with the corresponding encoding 
AS
BEGIN   
  IF CharIndex('ß', @string) > 0  
     SET @string = replace(@string, 'ß', 'ß')	

  IF CharIndex('ö', @string) > 0  
     SET @string = replace(@string, 'ö', 'ö')	

  IF CharIndex('Ö', @string) > 0  
     SET @string = replace(@string, 'Ö', 'Ö')	
    
  IF CharIndex('ü', @string) > 0  
     SET @string = replace(@string, 'ü', 'ü')	

  IF CharIndex('Ü', @string) > 0  
     SET @string = replace(@string, 'Ãœ', 'Ü')	

  IF CharIndex('ä', @string) > 0  
     SET @string = replace(@string, 'ä', 'ä')

  IF CharIndex('Ä', @string) > 0  
     SET @string = replace(@string, 'Ä', 'Ä')
                                
  RETURN (LTrim(RTrim(@string)))
END


--test the function 
SELECT [dbo].[ReplaceCodes2Umlauts]('Falsches Ãœben von Xylophonmusik quält jeden größeren Zwerg')

In the inverse scenario, at least for the German language is possible to replace the umlauts with a set the corresponding transliterations:

-- drop the function 
DROP FUNCTION IF EXISTS [dbo].[ReplaceUmlauts]

-- create the function 
CREATE FUNCTION [dbo].[ReplaceUmlauts](
  @string nvarchar(max)
) RETURNS nvarchar(max)
-- replaces umlauts with corresponding transliterations 
AS
BEGIN   
  IF CharIndex('ß', @string) > 0  
     SET @string = replace(@string, 'ß', 'ss')	

  IF CharIndex('ü', @string) > 0  
     SET @string = replace(@string, 'ü', 'ue')	

  IF CharIndex('ä', @string) > 0  
     SET @string = replace(@string, 'ä', 'ae')	
    
  IF CharIndex('ö', @string) > 0  
     SET @string = replace(@string, 'ö', 'oe')	
                                
  RETURN (LTrim(RTrim(@string)))
END

--test the function 
SELECT [dbo].[ReplaceUmlauts]('Falsches üben von Xylophonmusik quält jeden größeren Zwerg')

Happy coding!

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