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Empower your Business Intelligence solutions with Microsoft’s SSIS, one of the leader tools of data integration field.
With its ability to perform broad range of data integration tasks, SSIS offers an efficient and flexible way to implement Extract Transform and Load (ETL) solutions for the most common Data Warehousing scenarios.
Microsoft offers SSIS Project Designer support as an extension in Visual Studio in which SSIS projects and packages are created and deployed.
Today data is stored in many different locations in many different formats. For a fast and efficient data integration solution, business users need a tool that can extract data from all these different sources and load it into a desired repository (Data Warehouse, Data Mart, Hub etc.).
SSIS can connect to many different data source systems. Below some known examples of these data sources are listed:
Data preparation is one of the most important steps of BI solutions as data isn’t always collected and stored in its best version or in the way that applies to business needs.
Transformation of data is required to enable business users to be able to retrieve meaningful information from the data according to their needs. SSIS enables you apply a wide range of data transformations such as data aggregation, data type conversion, sorting, creation of derived columns, joining tables, lookups, pivoting/unpivoting columns and many more.
Business users need up-to-date data; SSIS can handle this by scheduled ETL jobs in SQL Server and keep the SQL Server databases up to date without any user intervention. It only takes to set the scheduled jobs through Integration Services Catalogs which connect to SSIS packages and execute them in the days and/or times set. Jobs can be scheduled daily, weekly and monthly as well as they can occur multiple times in a day. It can also be set to send automatic notification in case of success or failure.
There may be plans to move the data to Azure Cloud in the future. Now it is possible deploy, run and manage SSIS projects and packages on Azure SQL Database or SQL Managed instance with known tools such as SQL Server Management Studio (SSMS).
Alternatively, it is possible to proceed with a Hybrid ETL solution as in below example provided by Microsoft:
Microsoft Azure Data Factory is a cloud service that helps you create, schedule, and manage data pipelines to move and transform data.
With Azure Data Factory, you can bring together data from different sources and use various data processing services to transform it.
You can deploy and run SSIS packages in Azure Data Factory, which allows you to make use of your existing SSIS investments while taking advantage of the scalability and flexibility of Azure. This integration makes it easy to move data between on-premises and cloud systems and utilize the full range of data processing services available in Azure.
Integrating Microsoft SQL Server Integration Services (SSIS) into Microsoft Azure Data Factory provides a flexible and powerful platform for data integration and transformation. It lets you leverage your existing SSIS investments while benefiting from the scalability and flexibility of Azure.
Webinar: Data Warehouse with MS Azure Data Factory
After having the data from different sources integrated, cleaned and prepared, structured in a data warehouse according to business needs in SSIS, it can be connected to Qlik or Power BI. A well-prepared data and in a well-structured data warehouse can easily lead to high-performance BI solutions with these tools which provide insights from right data to right users at the right time.
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