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
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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:
Parameters
Azure Data Factory (ADF)
SSIS
Overview
Azure’s cloud ETL service for scale-out serverless data integration and data transformation
An on-premises tool for creating enterprise-level data integration and data transformation solutions
Azure Data Factory Vs. SSIS Performance
It is swift in performance since it works with the cloud-based architecture.
Very fast in performance, data transformation is done in memory buffers
Variety of Data
Structured and unstructured data
Structured data
Programming Languages
ADF does not have a programming SDK – It uses PowerShell, Python, and .NET
SSIS has a programming SDK that uses automation through C#, BIML, VB
Development Tools
Web browser
SQL Server development tools
Costing Structure
Abides by the ‘pay as you go’ model
It has free and paid licensed versions
Utilization
ETL, ELT, data movement, orchestration, Reverse ETL, streaming
ETL, ELT, data integration, data transformation, Reverse ETL
Database Replication
Full and incremental load
Full and incremental load
Addition of New Data Sources
Offers SDK for creating custom connectors
By coding custom data source elements
Data Connectors
Over 90+ inbuilt data connectors for data integration
Various data connectors are compatible with .NET, ADO, ODBC, OLEDB, etc.
Developer Tools
Azure Portal, CLI, PowerShell, Visual Studio
Visual Studio, SQL Server Management Studio
Purchase Details
Self-service purchase via Azure portal or Microsoft sales team
Bundled along with Microsoft SQL Server
Version Control
It can be integrated into Azure DevOps git or GitHub and supports branching, merging, etc.
It can be integrated with TFS and Git, supporting team-based development with merging and branching.
Velocity of Data
Streaming, real-time, and batch
Batch
Data Flows
It uses Apache Spark with optimization features, which makes it suited for more extensive data sets.
Best suited for small to medium data sets because the startup time is more than the runtime
Learning Curve
It is still evolving; hence, it needs time to master
Since it has been around for a while, it is easy to learn
Support for Triggers
ADF supports scheduled batch, event-based, and tumbling window triggers
SSIS supports batch triggers only but can help in developing custom triggers for data streams
Data Integration
It integrates with various cloud-based tools and other data sources like Azure Data Lake Store.
Integrates with a range of connectors, though they may not be cloud-based
User Interface
An effective graphical user interface for designing workflows executable in the cloud or on-premises
Graphical interface with drag-and-drop components for the creation of pipelines
Architecture
Hosted entirely in the cloud, networks are stateless
On-premises tool that executes in the data center
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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