Microsoft SQL Server Integration Services (SSIS)
We are well aware of the three major services provided by Microsoft SQL Server which are as follows
- SQL Server Integration Services (SSIS)
- SQL Server Analysis Services (SSAS)
- SQL Server Reporting Service (SSRS)
In the previous blog, we discussed SQL Server Reporting Services (SSRS). Here we would be specifically discussing SQL Server Integration Services (SSIS).
Need for Data Integration
Suppose there is a case where we have various departments in an organization and the manager is in search of a department which makes the maximum revenue per month or in a specific time period.
Can you think of a way?
There can be a way where the manager asks for the data from each department and then figures out which department is doing good in terms of revenue. This could have been an option if there would be no existing method but we do have a process where the tedious task of the manager could be made easy. The solution to this tedious task is Data Integration.
What is Data Integration?
Data integration is the practice of consolidating data from disparate sources into a single dataset with the ultimate goal of providing users with consistent access and delivery of data across the spectrum of subjects and structure types and to meet the information needs of all applications and businesses processes. It can also be understood as in which structured, semi-structured, and unstructured data is retrieved and combined as an incorporated form and structure.
We also have various methods for data integration such as data modeling, data profiling, data cleansing, etc.
Advantages
Data Integration has a lot of advantages, a few of them are as follows
Reduces Complexity
Whenever the data is brought to one place from many other sources it is very obvious that it is going to solve the complexity issue. It would be very easy to read and analyze data that were previously at different locations and now are integrated and brought to one place.
Data Integrity
Data Integrity is another important aspect of Data integration. Whenever the data is brought to one place from different sources it is important to give an integrity check. It should be checked that the data brought from the other sources are similar in terms of quality and quantity.
Easy Data Collaboration
During data integration, the data is assembled at a single location therefore the collaboration of any sort of data turns out to be really easy. The data can easily be picked up for different use or further collaborations.
Smarter Buisness Decisions
While the data is brought into a single container it is quite easy to make impactful decisions as one has numbers and can talk facts based on those numbers therefore data integration could greatly help in making smarter business decisions.
Why SSIS?
- The SSIS is popular because the data which has to be integrated can be loaded to many parallel to many varied locations. This saves time and provides an edge to SSIS over other tools.
- In order to integrate data there comes a need of hardcore programming and to eliminate the need for these hard-core programmers and cut down the cost of the organization SSIS is helpful.
- If the product line is entire of Microsoft, SSIS ensures tight integration along with the other product of Microsoft.
- When we talk about cost SSIS is comparatively much cheaper than most the ETL tools.
What is SSIS?
SSIS is a component of Microsoft SQL Server database software that can be used to perform a broad range of data integration and data transformation tasks. It is also used for data integration and workflow of applications.
How does SSIS work?
There are three major components of SSIS
Operational Data Store: Here the data is multiple sources for additional operations on the data. It is the place where data is housed before being transferred to the warehouse.
ETL: ILt is expanded as extract, transform and load the data when loaded from the different sources of the same.
Data Warehouse: Assembling data from different sources. It also helps in making smart business decisions.
ETL
The ETL (Extract, Transform, Load) specifically has four steps to extract the data from different sources, transform the extracted data and then finally load it in the form the machine needs.
- Capture: Here the source data or the metadata is picked in any format and brought into use.
- Scrub: This part mainly identifies the errors in the original data. It uses some AI techniques to find the errors in the original data and verifies the quality of data.
- Transformation: Here the source data or the metadata which is already captured and scrubbed is converted to the required format.
- Load and Index: Here the data is finally loaded into the data warehouse. Indexing helps to track the number of rows stored in the warehouse.
SSIS Package
It is an object that implements integration services functionality to extract transform and load data.
The package has three sub-components in it as follows
- Connections
- Control flow elements
- Data flow elements
