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Data quality becomes increasingly important in an age where data is the primary currency for businesses. Ensuring data quality is fundamental to the reliability of business decisions, and to the overall success of data-driven strategies. In this post, we will discuss how to implement quality audits on your data using SQL Server Integration Services (SSIS).

SQL Server Integration Services is a component of Microsoft SQL Server, used for building enterprise-level data integration and data transformation solutions. It is a platform for data integration and workflow applications. SSIS was first introduced with SQL Server 2005 and has been integral to every version since. It is used for extracting, transforming, and loading data (ETL).

Implementing a data quality audit with SSIS requires the use of several tasks within the SSIS toolbox. The first step is to use the data flow task to extract data from the source. For SQL Server, this can be done using the OLE DB Source, while for MySQL 5.7 and DB2 11.1, the ADO.NET Source would be appropriate.

-- SQL Server 2016 or 2017
SELECT * FROM [Source Table]
-- MySQL 5.7
SELECT * FROM `Source Table`
-- DB2 11.1
SELECT * FROM "Source Table"

Once the data has been extracted, it needs to be transformed to fit the destination's schema. The destination could be a SQL Server 2016 or 2017, Azure SQL database, MySQL 5.7, or DB2 11.1 database. In this process, data type conversions, aggregations, and other transformations may be required. The transformations can be implemented using the various tasks and transformations in the SSIS toolbox.

After transformation, the data should be loaded into the destination database. This is done using the data flow task, similar to the extraction step. For SQL Server and Azure SQL, the OLE DB Destination can be used, while for MySQL 5.7 and DB2 11.1, the ADO.NET Destination would be appropriate.

-- SQL Server 2016 or 2017, Azure SQL
INSERT INTO [Destination Table]
SELECT * FROM [Transformed Data]
-- MySQL 5.7
INSERT INTO `Destination Table`
SELECT * FROM `Transformed Data`
-- DB2 11.1
INSERT INTO "Destination Table"
SELECT * FROM "Transformed Data"

To ensure quality, the data should be audited right after the extraction and transformation steps. The audit can check for missing data, duplicate data, or inconsistent data formats. This can be done using the Data Profiling task in the SSIS toolbox. The task is configured to use the data from the extraction and transformation steps as input, and the output is a report detailing the audit results.

Data quality audits are essential in ensuring the reliability and usefulness of the data. By using SQL Server Integration Services, you can implement robust and efficient audits that will help you maintain high data quality. Whether you are using SQL Server, MySQL, DB2, or Azure SQL, SSIS provides the tools you need to audit your data effectively.