Azure Data Factory vs SSIS: Which is a Better ETL Tool?

As data integration and migration processes take place, selecting the apt ETL (Extract-Transform-Load) tool is vital. Involved professionals look for the tool that offers them optimal results. Two big names in the world of ETL are Azure Data Factory (ADF) and SQL Server Integration Services (SSIS).

These tools are created to read from disparate data sources and then write and transform data. Hence, both point towards a similar objective but have their specialties.

The below comparison between Azure Data Factory and SSIS is especially helpful for those having a tough time deciding on the ETL tool. The evaluation based on different parameters, advantages, and limitations illuminates each tool’s characteristics and situations in which it is a better performer.

First, let us review them individually, including their pros and cons.

What is Azure Data Factory?

Azure Data Factory is a famous data orchestration and ETL tool vital to Microsoft Azure development. It assists organizations in designing on-premises or cloud-driven solutions.

As a serverless technology, it empowers users to undergo enterprise-level data movements and transformations by transferring data through different layers. It assists in creating and scheduling pipelines that can collect data from disparate sources and then perform data transformation on big data.

Azure Data Factory has four main components:

• Pipeline for the jobs that are to be executed.
• Activity for all independent steps in a pipeline.
• Datasets representing databases/files/folders as input to the ETL process
• Linked Services is a connection string connecting data sources and services with authentication.

As a managed cloud-driven data integration service, it builds on the Reliable Services framework woven into the Microsoft Azure platform.

What is SSIS?

SSIS stands for SQL Server Integration Services, a popular platform for performing enterprise-level data transformation and integration jobs. It can be hosted on-premises in a self-managed virtual machine or deployed as part of ADF in a virtual machine that Microsoft manages. It is a tool that offers joins, aggregation, splits, etc.

As a flexible and fast data warehousing tool leveraged for ETL, it undergoes data migration activities that can quickly transfer and transform data from one source to another. The data sources it supports are Oracle, SQL, Excel files, DB2 databases, etc. Developers can build innovative solutions without creating a single line of code.

SSIS possesses four significant components:

• Control Flow that assists in arranging the order of components.
• Data Flow performs the ETL process to load onto another destination.
• Packages collect the control and data flow activities
• Parameters that simplify the task of passing runtime values to SSIS packages.

SSIS vs ADF: Pros and Cons

Pros of SSIS:

• Standardized ETL solutions for daily tasks
• Capability to manage and connect data from disparate sources
• User-friendly, easy to use, and learn for novices
• Good debugging facilities
• Simple Active Directory integration for SQL Server connect
• Create outbound data files and record them to FTP
• Continuous Integration and Continuous Deployment
• It is easy to take data from servers other than MS SQL Server or Oracle
• Offers self-service BI facilities
• Lookup and transformation functionalities

Cons of SSIS:

• Parallel execution of multiple packages
• Limited flat file or Excel connection
• Challenging to map destination with OLE DB command

Pros of ADF:

• Offers good support for exhaustive queries
• Serverless solution lessening meticulous tasks and maintenance
• Seamless integration with third-party connectors
• It is easy to create pipeline schedules and execute SSIS/SSMS packages
• Copies data from JSON, Azure database, API, Azure Synapse, etc.
• Usage of linked service in many pipelines
• Fast, convenient with multiple connectors
• Easy setup of security protocols for IP addresses
• Effective monitoring with fine-tuning, automation, and flexibility
• Access to multiple users from remote locations

Cons of ADF:

• Less flexible as compared to other ETL tools
• No debugging tools
• Limited source connectors

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Organizations Using Them

Companies Using ADF:

KPMG, EY, GEICO, Ryder System, Lenovo Group Ltd., Blue Cross Blue Shield Association and many more

Companies Using SSIS:

Humana, Wells Fargo, KPMG, CVS Health, Uline, Guidehouse, NetSuite Inc., Red Hat Inc., and many more

Azure Data Factory vs SSIS: The Differences

As you compare SSIS vs. Azure Data Factory, we observe that both are leading, scalable, accessible, secure, and robust data integration tools heavily influenced by graphical user interfaces.

Both are leveraged for ETL activities that consist of disparate sources and sinks. But specific characteristic differences set them apart and ease out the task of choosing the right one; here are they:

On a Concluding Note

As we go through the detailed comparison of Azure Data Factory vs. SSIS, we feel that both are competent enterprise ETL tools and can be implemented as organizations need. Confident helping tips can aid in decision-making:

You can choose SSIS when

• The work area is on-premises
• Users already possess SQL Server 2016 or later
• ETL processes are to be executed continually
• You have Visual Studio and Windows development machines

You can choose ADF when

• You want to select the ‘pay as you use’ model
• Most of your activities are in the cloud
• Activities are spread in different time zones
• You use an operating system like iOS other than Windows

Specific parameters can help you decide which to select when assessing SSIS vs ADF. They could be cloud infrastructure, data size, budget estimates, project deadlines, availability of skilled resources, and infrastructure.

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