Data Lineage is a process of understanding data’s lifecycle, from origin to destination. It tracks where data originates, how it flows through organization systems, and how it changes. Data lineage is crucial for understanding data management, metadata, and data analytics. It provides valuable information for effective data usage and analysis.

One of the immediate benefits of data lineage is better and more accurate analytics. By knowing where data comes from and what it means, analytics teams and business users can find the data they need for business intelligence and data science purposes. This leads to better analytics results and enables data-driven decision-making.

Data lineage also plays a significant role in data security and privacy. Organizations can use data lineage information to identify sensitive data that requires strong security measures and assess potential risks. It helps in strengthening data governance and tracking data throughout its lifecycle.

In addition to improving data quality, data lineage also enhances data management tasks such as data migration, data consolidation, and detecting potential data-related problems. It provides insights into data engineering and IT tasks, making them more efficient and effective.

To analyze and collect information about data sources and data flow in SQL Server, you can use a data lineage script written in T-SQL. This script provides a simplified view of the SQL query and helps in documenting end-to-end mappings and data flows within your organization’s systems.

The data lineage script consists of three main parts:

1. A standalone function for removing unnecessary or irrelevant characters from the lineage
2. A section to remove comments from the SQL query
3. A loop to analyze the data sources and corresponding clauses in the query

The script removes unwanted characters, extracts predicates and tables, and returns all the relevant information regarding data sources for your query.

Here is an example of the data lineage script:

CREATE OR ALTER FUNCTION dbo.fn_removelistChars
(
    @txt AS VARCHAR(max)
)
RETURNS VARCHAR(MAX)
AS
BEGIN
    DECLARE @list VARCHAR(200) = '^a-zA-Z0-9+@#\/_?!:.''-]'
    
    WHILE PATINDEX(@list,@txt) > 0
        SET @txt = REPLACE(cast(cast(cast(cast(cast(cast(@txt as nvarchar(max)) as nvarchar(max)) as nvarchar(max)) as nvarchar(max)) as nvarchar(max)) as nvarchar(max)),cast(cast(cast(cast(cast(cast(SUBSTRING(@txt as nvarchar(max)) as nvarchar(max)) as nvarchar(max)) as nvarchar(max)) as nvarchar(max)) as nvarchar(max)),cast(cast(cast(cast(cast(cast(PATINDEX(@list,@txt as nvarchar(max as nvarchar(max as nvarchar(max as nvarchar(max as nvarchar(max as nvarchar(max))))))))))))),1),'')
    
    RETURN @txt
END;

CREATE OR ALTER PROCEDURE dbo.TSQL_data_lineage 
(
    @InputQuery NVARCHAR(MAX) 
)
AS
BEGIN
    -- Remove comments characters
    -- Create data lineage for inputed T-SQL query
    
    -- Code for removing comments
    
    -- Code for creating data lineage
    
    -- Final results
    
END;

To use the data lineage script, you need to create a procedure and provide your T-SQL query as an input parameter. The script will remove comments from the query and create data lineage based on the data sources and clauses used in the query.

Here is an example of how to run the data lineage script:

DECLARE @test_query VARCHAR(MAX) = '
-- This is a sample query to test data lineage
SELECT 
    s.[BusinessEntityID]
    ,p.[Title]
    ,p.[FirstName]
    ,p.[MiddleName]
    ,p.[Suffix]
    ,e.[JobTitle] as JobName
    ,p.[EmailPromotion]
    ,s.[SalesQuota]
    ,s.[SalesYTD]
    ,s.[SalesLastYear]
FROM [AdventureWorks2014].sales.[SalesPerson] s
    LEFT JOIN [AdventureWorks2014].[HumanResources].[Employee] e 
    ON e.[BusinessEntityID] = s.[BusinessEntityID]
INNER JOIN [AdventureWorks2014].[Person].[Person] AS p
ON p.[BusinessEntityID] = s.[BusinessEntityID]
'

EXEC dbo.TSQL_data_lineage 
  @InputQuery = @test_query

The data lineage script will return the results of the tables and columns used in the query, providing valuable insights into the data sources and data flow.

The script is compatible with SQL Server 2016 and later versions, including Azure SQL Server, Azure SQL Database, Azure MI, and Azure Synapse. It can be used in all editions of SQL Server.

By understanding data lineage and implementing data governance practices, organizations can address data quality issues, improve data analysis, and enhance data security. The data lineage script discussed in this article can be a valuable tool in achieving these goals.

Start leveraging data lineage in your SQL Server environment today and gain better control and visibility over your data.