Handling Schema Drift in Modern ETL Pipelines
By Tom Nonmacher
Schema drift is a term used to describe the phenomenon where the structure of a database changes over time. This can be a result of both intentional and unintentional actions such as adding or deleting columns, changing data types, or modifying constraints. It can pose a significant challenge in maintaining ETL (Extract, Transform, Load) pipelines as it can lead to data inconsistency, pipeline failures and can ultimately impact business decisions. In this article, we will discuss how to handle schema drift in modern ETL pipelines using various technologies such as SQL Server 2012, SQL Server 2014, MySQL 5.6, DB2 10.5, and Azure SQL.
The first step in managing schema drift is detection. An effective way of detecting schema drift is by storing metadata about your database and comparing it to the current schema. SQL Server provides a system view called INFORMATION_SCHEMA.COLUMNS that can be used to retrieve metadata about columns in a given table. Here is an example of how this can be done:
SELECT COLUMN_NAME, DATA_TYPE, IS_NULLABLE FROM INFORMATION_SCHEMA.COLUMNS
WHERE TABLE_NAME = 'YourTable'
By regularly comparing the results from this query to a stored version, you can detect any changes in the schema. MySQL 5.6 also provides similar functionality using the information_schema.columns table.
Once schema drift has been detected, you will need to handle it in your ETL pipeline. Most ETL tools provide some sort of mechanism to handle schema drift. For example, in SQL Server Integration Services (SSIS), a common method of handling schema drift is by using the Script Component and a bit of .NET code to dynamically generate column mappings. Azure Data Factory, on the other hand, provides native support for handling schema drift.
In situations where you are using an ETL tool that does not provide native support for handling schema drift or if you are writing your own ETL process, you will need to handle schema drift manually. This can be done by writing code that can dynamically adjust to changes in the schema. For example, when loading data into a table, instead of hardcoding column names, you can retrieve them dynamically:
DECLARE @cols AS NVARCHAR(MAX), @query AS NVARCHAR(MAX)
SELECT @cols = STUFF((SELECT ',' + QUOTENAME(c.name) FROM sys.columns c WHERE c.object_id = OBJECT_ID('YourTable') FOR XML PATH(''), TYPE).value('.', 'NVARCHAR(MAX)'), 1, 1, '')
SET @query = 'INSERT INTO YourTable (' + @cols + ') SELECT ' + @cols + ' FROM SourceTable'
EXEC sp_executesql @query
In conclusion, while schema drift can make maintaining ETL pipelines challenging, by effectively detecting and handling schema drift, you can ensure the consistent and reliable operation of your ETL pipelines. Whether you are using SQL Server, MySQL, DB2, or Azure SQL, all provide the necessary tools to manage schema drift effectively.