A few years back, organizations discovered the power of analytics for their business, customer, marketing, sales, and operations data. They understood that the data that they generated on a daily basis was a gold mine in terms of making data-driven business decisions, building amazing products, and understanding their customers better.
Since then, the industry of analysis observed a boom and every organization wanted to analyze its data and make the best use of it. They derived insights from the data and increased their efficiency by making the right decisions.
Handling such a huge amount of data comes with a con. There is a good possibility the data might be lost or misused. It is hard to maintain data integrity and optimizing storage for an exponentially increasing data load is a challenging task. So to tackle this, organizations analyze relevant data and store the analyzed data elsewhere (some of the data may have a one-time use). So as a result a lot of movement of data is involved and this is where ETL tools come into the picture.
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.
Table of Contents
What is ETL?
Before talking about how to set up an SSIS ETL, you need to understand what an ETL tool does. ETL stands for Extract, Transform, Load. ETL is the process of extracting data from a source, transforming it, and loading it into a destination. It can simply be termed as moving data from one place to another. Sometimes moving data is easy. For example, moving data from one text file to a csv file.
What are the Advantages of ETL Tools?
There are a lot of advantages of an ETL tool. Some of them are listed below:
- Makes the regular task of moving data much easier to implement and manage.
- It helps transform and map data seamlessly to the desired destination.
- ETL tools have interfaces that are easy to work with in contrast to the traditional method of writing scripts to move data.
- Using an ETL tool gives you an idea about where your data is in the pipeline.
- Makes error detection easy.
- Helps you integrate a large number of sources and destinations.
- Saves a huge amount of time, effort, and resources.
- Validates your data before ingestion.
- Automates the whole process.
- Easily handles Big Data and helps reliably set up a Data Warehouse.
What is Microsoft SSIS ETL?
SSIS stands for SQL Server Integration Services software developed by Microsoft. SSIS ETL deals with data transformation and data integration. You can load data into Data Warehouse, perform data mining, data cleansing, etc using SSIS.
It can extract data from Flat Files, XML files, SQL databases, etc. It provides GUIs for performing transformations, integrations, and building packages. It also has Graphical Integration Service Tools that help perform all these tasks without writing a single line of code.
Read more about SSIS in the official documentation
Hevo, a No-code Data Pipeline helps to transfer your data from multiple sources (among 100+ sources) to the Data Warehouse/Destination of your choice to visualize it in your desired BI tool. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also takes care of transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
It provides a consistent & reliable solution to manage data in real-time and you always have analysis-ready data in your desired destination. It allows you to focus on key business needs and perform insightful analysis using a BI tool of your choice.
Check out Some of the Cool Features of Hevo:
- Completely Automated: The Hevo platform can be set up in just a few minutes and requires minimal maintenance.
- Real-Time Data Transfer: Hevo provides real-time data migration, so you can have analysis-ready data always.
- 100% Complete & Accurate Data Transfer: Hevo’s robust infrastructure ensures reliable data transfer with zero data loss.
- Scalable Infrastructure: Hevo has in-built integrations for 150+ sources that can help you scale your data infrastructure as required.
- 24/7 Live Support: The Hevo team is available round the clock to extend exceptional support to you through chat, email, and support calls.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Live Monitoring: Hevo allows you to monitor the data flow so you can check where your data is at a particular point in time.
You can try Hevo for free by signing up for a 14-day free trial.
History of SSIS
Data Transformation Services was replaced by SSIS when after the first release of Microsoft Server 2005. Earlier DTS was included in all the versions but SSIS is available for selective editions. Then the next release was SQL Server 2008 that came with new sources and updates.
SQL Server 2012 was a major release for SSIS as it came up with the new concept of a project deployment model that allows users to deploy the entire project on the server unlike individual packages as before. SQL Server 2014 and 2016 didn’t come up with any major updates in the SSIS except for some additional sources.
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
With the release of the new version of SSIS, it 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 that have 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 transformations and functions that are built-in, and developing software is easier to use. It comes with various types of transformation that include BI (Business Intelligence) Transformations, Row Transformations, Split and Join Transformations, Rowset Transformations, Auditing Transformations, and Custom Transformations.
Each transformation also provides different kinds of 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 activity by standardizing it, correcting the data, and filling the missing values. The SSIS uses Fuzzy Lookup Transformation to locate the 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 is used to profile data in the server and perform 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.
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 in SSIS ETL
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 in SSIS ETL
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 and 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 on the OK button.
Step 3: Configuring the OLE DB Connection Manager in SSIS ETL
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 in SSIS ETL
Data flow Task helps in cleaning, transforming, and moving data using SSIS ETL.
To add a Data Flow task go to the Control Flow tab. Now in the SSIS Toolbox pane, expand Favorites. In the Control Flow tab drag a Data Flow 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. In the Properties window, verify that the LocaleID property is set to English (United States).
Step 5: Configuring the Source in SSIS ETL
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.
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 and select the required columns and check the column names and click on the OK button.
Step 6: Configuring Lookup Transformations in SSIS ETL
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 in SSIS ETL
When you reach this step your data is in a transformed 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 together 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, and then click on the OK button. Rename your destination component. Now double-click on the component and configure the destination in the Editor dialog box. Now click on the Ok button.
Pros and Cons of SSIS
Companies should choose SSIS as per their business needs. Many companies today use SSIS. A few advantages and disadvantages of SSIS are listed below:
- 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.
- 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.
Why is SSIS a Good ETL Tool for You?
SSIS is used by many big enterprises 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 has large and complex volumes of data. Apart from SSIS, there are many ETL tools available in the market with much less complexity such as Hevo.
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!