As the processes of data integration and data migration take place, selecting the apt ETL (Extract-Transform-Load) tool is a vital element. 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).
Both these tools are created for reading from disparate data sources and then writing and transforming data. Hence, both point towards a similar objective but have their own specialties.
The below comparison between Azure Data Factory vs SSIS is especially for those having a tough time deciding on the ETL tool. The evaluation based on different parameters, their advantages, and limitations throws light on the characteristics of each and situations in which they are better performers.
Firstly, let us go through each one of them individually with their pros and cons.
Azure Data Factory is a popular data orchestration tool and ETL tool that plays a vital role in 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 the creation and scheduling of 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 as 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 that is woven into the Microsoft Azure platform.
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 virtual machine that is self-managed or deployed as a 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 that is 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.
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As you compare SSIS vs Azure Data Factory, we observe that both are leading, scalable, accessible, secure, and robust data integration tools that are heavily influenced by graphical user interfaces.
Both are leveraged for ETL activities that consist of disparate sources and sinks. But there are certain characteristic differences that 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||Very fast in performance, since 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 – 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||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 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||Can be integrated into Azure DevOps git or GitHub, and supports branching, merging, etc.||Can be integrated with TFS and Git, supports team-based development with merging and branching|
|Velocity of Data||Streaming, real-time and batch||Batch|
|Data Flows||Makes use of Apache Spark with optimization features hence suited for bigger 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 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||Integrates with a variety of cloud-based tools and other data sources like Azure Data Lake Store etc.||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|
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 needed by organizations. There are certain helping tips that can aid in decision-making:
You can choose SSIS when
You can choose ADF when
There are certain parameters that can help in deciding which one to select from when you assess SSIS vs ADF. They could be the presence of cloud infrastructure, size of data, budget estimates, project deadlines, availability of skilled resources, and infrastructure.
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