SQL Server Integration Services (SSIS) is a Microsoft SQL Server database built to be a fast and flexible data warehousing tool to perform high-performance data integrations. SSIS can be used for extraction, loading, and transformation (ETL) of data by extracting data from multiple sources, such as SQL Server database, Oracle database, and Excel files. It uses cleaning and merging processes to help make data more informative.
A primary responsibility of SQL Server Integration Services is the migration of data from different sources to other destinations. It also offers a wide range of tools and solutions, including a data warehousing tool for ETL, to assist in data integration and workflow activities. The most common uses of SSIS include:
- Data archiving: Merging data into a single dataset is one of the most common practices. Businesses usually archive information they no longer need for regular operations. In this case, SSIS is used to homogenize the information. It can seamlessly handle huge volumes of data coming from different sources. SSIS can transform archived information into a valuable data source by splitting and merging to make it a powerful asset for the enterprise.
- Data loading (bulk-load data): Another challenge businesses face is maintaining over-populated data warehouses and marts. In these data warehouses, the data volume is enormous, while the time given for extraction, loading, and transformation of data is less. SSIS includes a destination component designed to bulk-load the information directly from flat files stored in the SQL database or perform a bulk load into SQL Server. It also includes checkpoints to rerun a package and quickly handle various types of errors that may occur during complex data-loading scenarios. SSIS is capable of denormalization that helps source data from a particular destination such as tables or files.
- Data indexing or history management: History management is crucial within your data warehouses to review the actual state of processes at a specific time. To manage such complex updating scenarios, SQL Server Integration Services uses the "Slowly Changing Dimension Wizard." This wizard enables you to dynamically create and configure data transformation tasks, such as adding or updating records, adding new tables, columns, and rows to simplify and streamline history management.
- Data Cleansing: A data-quality check is another important step businesses need to perform. As they receive data from multiple external and internal sources, it becomes essential to standardize and clean the data before loading it into the systems. Different business areas use different data standards and formats to store information. To standardize all the information, you can use SSIS to perform data transformation tasks such as cleaning, converting, and enriching. You can also identify duplicate records using the SSIS grouping transformation feature to remove such records before data loading.
SSIS package monitoring is important to understand how the components work. SSIS package monitoring includes configuring the logging of performance counters. The counters enable you to view how resources are used and consumed during the execution of an SQL Server Integration Services package. Helpful counters to use include:
- Rows read: This counter allows you to count the number of rows as they pass through a data flow and provide the final count.
- Buffers in use: This counter provides the pipeline details in the buffer used throughout the package pipeline.
- Buffers spooled: This enables you to track when your machine is running out of physical or virtual memory during a data flow process by determining the number of buffers used.
You can process data from more than 160 applications using SSIS.