Azure Data Factory vs SSIS comparison

Azure Data Factory is a managed cloud service built for extract-transform-load (ETL), extract-load-transform (ELT), and data integration projects. This is a digital integration tool as well as a cloud data warehouse that allows users to create, schedule, and manage data in the cloud or on premises. The use cases of the product include data engineering, operational data integration, analytics, ingesting data into data warehouses, and migrating on-premise SQL Server Integration Services (SSIS) packages to Azure.

The tool allows users to create data-driven workflows for initiating data movement and data transformation at scale. Data can be ingested from disparate data stores via pipelines. Companies can utilize this product to build complex ETL processes for transforming data visually with data flows. Azure Data Factory also offers services such as Azure HDInsight Hadoop, Azure Databricks, Azure Synapse Analytics, and Azure SQL Database. These services are created to facilitate data management and control for organizations, providing them with better visibility of their data for improved decision-making.

Azure Data Factory allows companies to create schedules for moving and transforming data into their pipelines. This can be done hourly, daily, weekly, or according to the specific needs of the organization. The steps through which the data-driven workflows work in Azure Data Factory are the following:

1. Connecting to required sources and collecting data. After connecting to the various sources where data is stored, the pipelines move the data to a centralized location for further processing.

2. Transforming and enriching the data. Once the data is moved to a centralized data store in the cloud, the pipelines transform it through services like HDInsight Hadoop, Azure Data Lake Analytics, Spark, and Machine Learning.

3. Delivering the transformed data to on-premise sources or keeping it in cloud storage sources for usage by different tools and applications.

Azure Data Factory Concepts

The solution consists of a series of interconnected systems that provide data integration and related services for users. The following concepts create the end product for users:

• Pipelines: A pipeline refers to the logical grouping of activities that performs a unit of work which together perform a task.
  
  
• Mapping data flows: Azure Data Factory lets its users create and manage graphs of data transformation logic for transforming any-sized data. The logic is executed on a Spark cluster, which does not have to be managed or maintained personally by the user.
  
  
• Linked services: The linked services in the tool define the connection to the data source. There are various services used for two main purposes - to represent a data store that the solution supports and to represent a compute resource that can host the execution of an activity.
  
  
• Integration runtime: The integration runtime in the tool provides the bridge between the activity and linked services needed for it.
  
  
• Triggers: There are various types of triggers in the solution, created for different types of events. They determine when a pipeline execution should be initiated.
  
  
• Pipeline runs: Pipeline runs are instantiated by passing the arguments to the parameters that are defined in pipelines, executing the pipelines' work.
  
  
• Control flow: Control flow in Azure Data Factory is an orchestration of pipeline activities.
  
  
• Connect and collect: This serves as the first step of the services that this tool offers. It connects all the required sources of data and processing in order to prepare the data for moving it to a centralized location for further processing. The step eliminates the need for companies to integrate expensive custom solutions for data movement. Through Copy Activity, Azure Blob storage, and Azure HDInsight Hadoop cluster, users can quickly initiate the first step of organizing their data.
  
  
• Transform and enrich: The collected data can be processed or transformed by using the mapping data flows of the product. Data transformation graphs can be executed on Spark without the need to understand its clusters or how programming works.
  
  
• CI/CD and publish: Through Azure DevOps and GitHub clients, the tool can receive full support for CI/CD for their data pipelines, which allows for the development and delivery of ETL processes before publishing the finished product.
  
  
• Monitor: When users have successfully built and deployed their data integration pipelines, the service offers them the option to monitor the scheduled activities and pipelines. This is done through Azure Monitor, API, PowerShell, and health panels on the Azure portal.

Azure Data Factory Benefits

Azure Data Factory offers clients many several benefits. Some of these include:

• An easy-to-use platform which is suitable for both beginner and expert users, as it offers code-free processes and built-in support.
  
  
• Pay-as-you-go option for clients to pay only for the services that they are using.
  
  
• Powerful tool with more than 90 built-in connectors, which allow companies to ingest on-premise and software as service (SaaS) data quickly.
  
  
• Provided autonomous ETL, which unlocks operational efficiencies and citizen integrators.
  
  
• The tool is designed to handle large volumes of data and provide users with better scalability and performance than classic ETL systems.
  
  
• Azure Data Factory allows users to easily migrate ETL workloads to the solution’s cloud.
  
  
• The solution offers great security for its users, as it provides the option for assigning specific permissions and roles within the organization.
  
  
• Azure Data Factory is highly automated, which allows users to orchestrate their data more efficiently.
  
  
• The platform is a combination of GUI and scripting-based interfaces, which gives users more freedom over data management.
  
  
• The tool provides organizations with the option to rely on Microsoft to fully manage the process. This eliminates the potential need of hiring a third-party expert.

Reviews from Real Users

According to Dan M., a Chief Strategist & CTO at a consultancy, Azure Data Factory is secure and reasonably priced.

A Senior Manager at a tech services company evaluates the tool as reasonably priced, scales well, good performance.

SQL Server Integration Services (SSIS) is a Microsoft platform designed to build enterprise-level data integration and data transformation solutions. Users now have the ability to solve intricate business queries by downloading and copying files, cleansing and mining data, loading data warehouses, and managing SQL Server objects and data through Integration Services. SSIS is a component of the Microsoft SQL Server data software used for many data migration tasks.

SSIS loads data into one or more destinations by extracting and transforming data from a wide variety of sources such as XML data files, flat files, and relational data sources.

Integration Services include:

• An SSIS catalog database to manage, store, and run packages.
• An ample set of built-in transformations and tasks.
• Graphical tools for building packages.
  

Create packages programmatically and code custom tasks through programming the extensive Integration Services. With graphical Integration Services, you can create solutions without writing any code.

Benefits of SSIS

There are many benefits of SSIS, such as:

• Solve complex business problems - encrypt files with SSIS and send them to various network locations.
  
  
• Migration of DTS packages to SSIS - users can migrate DTS packages to SSIS while choosing to run DTS packages using DTS runtime or incorporate DTS packages into SSIS.
  
  
• Development of ETL processes - Microsoft’s SSIS packages provide the ability to extract, transform, and load data into data warehouses. This service takes data from various sources, like CSV files, XML files, flat files, and relational data sources and transforms and loads them to their destinations.
  
  
• Data migration from other databases - with Integrations Services, users can transform data to make sure it complies with the rules of the database they are migrating to.
  
  
• Managing and automating SQL server objects - SSIS packages can manage and automate SQL server objects which will help save you time and resources.

Reviews from Real Users

The SSIS platform stands out among its competitors for a variety of reasons. Two major ones are its debugging capabilities during data flow execution and its easy connectivity with other Microsoft tools.

Muhammad J., a senior manager software developer at Techlogix, notes, "The debugging capabilities are great, particularly during data flow execution. You can look into the data and see what's going on in the pipeline."

Ismail L., a data engineer at a tech service company, writes, "The most valuable thing is that it is easy to connect with Microsoft tools. In Europe, particularly in France, a lot of companies use Excel, SQL Server, and other Microsoft tools, and it is easier to connect SSIS with Microsoft tools than other products."