How To Set Up Microsoft SSIS ETL: 7 Easy Steps

SSIS (SQL Server Integration Services) is a powerful tool that simplifies the ETL process, making it easier to extract, transform, and load from various sources efficiently. Its intuitive drag-and-drop interface allows you to build complex data workflows without needing deep coding skills. With SSIS, you can streamline your data integration tasks, automate processes, and ensure that your data is accurate and ready for business insights.

In this article, we will briefly discuss ETL and its advantages. Then, you will learn about Microsoft SSIS ETL and how you can set up your ETL Pipeline with Microsoft SSIS.

Most Important Features of SSIS

SSIS comes with a plethora of features that help users manage data. A few features of SSIS are listed below:

1) Built-in Data Connectors

The release of the new version of SSIS supports many built-in connectors that allow users to establish connections with data sources using connection managers. 

It supports Text, XML, Excel sheets, relational databases with reference data, and analysis service databases. SSIS can connect to WMI (Windows Management Instrumentation), SMO (SQL Server Management Objects), messaging queues, and mail servers. SQL Servers are used for transfer tasks and temporary work tables.

2) Transformations and Functions

SSIS offers many built-in transformations and functions, and software development is easier to use. It comes with various types of transformation, including BI (Business Intelligence) Transformations, Row Transformations, Split and Join Transformations, Rowset Transformations, Auditing Transformations, and Custom Transformations.

Each transformation also provides different transformations for cleaning and mining data, creating and updating columns, etc. Split and Join transformations can distribute rows in various outputs.

3) Fuzzy Grouping and Lookup Transformation

Users can use Fuzzy Grouping to clean data by detecting near-duplicate data and canonical rows to standardize the data. The Fuzzy Transformation looks for near value based on the score value provided by the user. 

The Fuzzy Lookup Transformation performs data cleaning by standardizing it, correcting the data, and filling the missing values. The SSIS uses Fuzzy Lookup Transformation to locate similar near values in a reference table.

4) Data Profiling Tools

The data profiling tools that SSIS offers are the data profiling task and data profile viewer. The data profiling task profiles data in the server and performs data quality checks. All this process is accomplished by computing profiles so that users can learn more about the data source.

The data profiling viewer allows users to review the data profiling task and it supports drill-down capabilities that help users understand data quality.

Prerequisites

You need to make sure that you have the below software installed and set up before creating an SSIS ETL:

• Microsoft SQL Server
• Microsoft SSIS software
• Microsoft Visual Studio

Steps to Set up an ETL Package in SSIS ETL

Follow the below-given steps to create an SSIS ETL package:

• Step 1: Creating a New Project and Package
• Step 2: Configuring the Flat File Connection Manager
• Step 3: Configuring the OLE DB Connection Manager
• Step 4: Configuring the Data Flow Task
• Step 5: Configuring the Source
• Step 6: Configuring Lookup Transformations
• Step 7: Configuring the Destination

Step 1: Creating a New Project and Package

Open Microsoft Visual Studio. In the window, go to File, then go to New and click on Project. A Project Dialog Box pops up.

You can use existing templates or create a new one. In the Name box, provide the name for your project and browse the file location, and set your project location. Now click the OK button. By default, an empty package with the name Package.dtxs is created. Right-click on it to rename it.

Step 2: Configuring the Flat File Connection Manager

Flat File Connection Manager will help you extract data from Flat Files. Configuration of Flat File Connection Manager is different for different file formats.

To add the Connection Manager to your SSIS ETL project, go to Connection Manager in the Solution Explorer pane, right-click on it, and select New Connection Manager. Select Flat File from the dialog box that pops up and click on Add.

Now add the Connection Manager name and browse the Flat File source. Configure the source in the Solution Explorer pane and click OK.

Step 3: Configuring the OLE DB Connection Manager

OLE DB Connection Manager is used to connect to the data destination.

To add the OLE DB Connection Manager to your SSIS ETL, go to the Solution Explorer pane, right-click on the Connection Manager and select New Connection Manager. Select OLEDB from the dialog box that pops up and select Add.

In the Configure Connection Manager dialog box, click on New. Enter localhost as Server name. Set your connection and test the connection using Test Connection. Click on the OK button when done.

Step 4: Configuring the Data Flow Task

Data flow Task helps in cleaning, transforming, and moving data using SSIS ETL.

To add a dataflow task go to the Control Flow tab. Now in the SSIS Toolbox pane, expand Favorites. In the Control Flow tab, drag a dataflow task onto the design surface. Right-click the new Data Flow Task, select Rename, and provide a new name.

Right-click on the Data Flow task and select Properties. Verify that the LocaleID property is set to English (United States) in the Properties window.

Step 5: Configuring the Source

Select the Data Flow tab and go to the SSIS Toolbox. Expand OtherSources and add the Flat File source by dragging it into the design surface of the Data Flow tab.

Please right-click on the newly added Flat-file source and rename it. Double-click on the Flat File source and go to the Flat-file connection manager field from the editor dialog box. Select Flat File source data, select the required columns, check the column names, and click on the OK button.

Step 6: Configuring Lookup Transformations

A lookup transformation performs a lookup by joining data in the specified input column to a column in a reference dataset. The reference dataset can be an existing table or view, a new table, or the result of an SQL query.

Go to the SSIS Toolbox and expand Common. Now drag lookup onto the design surface of the Data Flow tab. You can now configure the lookup on the design surface to perform transformations.

Step 7: Configuring the Destination

When you reach this step, your data will be transformed into a format compatible with the destination. To configure the OLE DB destination, go to the SSIS Toolbox. In the toolbox, expand Other Destinations and drag the OLE DB Destination onto the design surface below the lookup transformation.

Connect all the components on the design surface to define the flow. Navigate to the Input Output Selection dialog box. In the Output list box, select Lookup Match Output, then click the OK button. Rename your destination component. Now, double-click on the element and configure the destination in the Editor dialog box. Now click on the Ok button.

Pros and Cons of SSIS

Companies should choose SSIS based on their business needs. Many companies today use SSIS. A few advantages and disadvantages of SSIS are listed below:

Pros

• It has a user-friendly graphical interface that makes it easier to use.
• It is easy to deploy and configure.
• It allows developers to save time by reusing the script across multiple projects.
• It offers connections to many data sources.
• It is easy to set up, manage, and configure projects and packages.

Cons

• It is not quite efficient with JSON.
• The learning curve is high for new users.
• Limited Excel connections.
• There are very few 3rd party tools that support SSIS.
• It involves complex coding and requires experienced developers.

Best Practices for SSIS ETL

• Keep packages modular: Break down large tasks into smaller, manageable packages.
• Use configurations: Externalize configurations to make packages easier to deploy across environments.
• Optimize data flow: Minimize the use of blocking transformations and optimize performance.
• Log everything: Set up logging to monitor the ETL process and quickly identify issues.
• Secure sensitive data: Encrypt sensitive information and use secure connections.

What SSIS Alternatives Can You Use?

Many big enterprises use SSIS and can easily manage complex data. Working with SSIS requires very skilled SQL developers because it involves coding in Visual Studio with big margin errors.

SSIS is a good choice for your company if you have significant and complex volumes of data. Besides SSIS, many ETL tools, such as Hevo, are available in the market with much less complexity. If you’re looking for a hassle-free, cloud-native, real-time ETL solution, Hevo is the better choice. SSIS suits Microsoft-centric environments but requires significant manual effort for scalability, maintenance, and cloud integrations.

Feature	Hevo	SSIS
Cloud-Native	Yes, fully managed	No, primarily on-premises
Ease of Use	No-code, intuitive UI	Requires coding & manual configurations
Real-Time ETL	Yes, supports real-time streaming	Limited, mainly batch processing
Data Sources	150+ pre-built connectors	Limited, requires additional connectors
Schema Handling	Auto-detects & adjusts dynamically	Manual schema updates required.
Scalability	Auto-scales with workload	Needs manual scaling & infrastructure
Error Handling	Built-in monitoring & auto-resolution	Manual error handling via scripts
Pricing	Pay-as-you-go, no hidden costs	Licensing fees + additional costs
Security	GDPR, HIPAA, SOC 2 compliant	Security depends on SQL Server setup
Maintenance	Fully managed, automatic updates	Requires manual updates & patching

Conclusion

Though you can create your pipeline with Microsoft SSIS ETL, it is a long and tedious process and you can easily be stuck despite following tutorials. It also houses support for limited source and destination options. So, use Hevo Data and spend your valuable time analyzing your data instead of working on these menial configurations.

Integrating and analyzing your data from a huge set of diverse sources can be challenging, this is where Hevo comes into the picture. Hevo is a No-code Data Pipeline and has awesome 150+ pre-built integrations that you can choose from.

Hevo can help you integrate your data from numerous sources and load them into a destination to analyze real-time data with a BI tool and create your Dashboards. It will make your life easier and make data migration hassle-free. It is user-friendly, reliable, and secure.

Check out the Hevo Pricing details here. Try Hevo by signing up for a 14-day free trial and see the difference!

FAQ