I can't go into specifics about the use case for Azure Data Factory, but it's for analytics related to an assessment.
Solution Architect at Mercedes-Benz AG
It lets you create ETL pipelines, and it comes with a good dashboard and many connectors
Pros and Cons
- "What I like best about Azure Data Factory is that it allows you to create pipelines, specifically ETL pipelines. I also like that Azure Data Factory has connectors and solves most of my company's problems."
- "A room for improvement in Azure Data Factory is its speed. Parallelization also needs improvement."
What is our primary use case?
What is most valuable?
What I like best about Azure Data Factory is that it allows you to create pipelines, specifically ETL pipelines.
I also like that Azure Data Factory has connectors and solves most of my company's problems. I can't recall a case where I couldn't use the solution for solving problems.
I'm also happy about the Azure Data Factory dashboard.
What needs improvement?
A room for improvement in Azure Data Factory is its speed. Parallelization also needs improvement. As for the rest of the features of Azure Data Factory, I'm happy.
I cannot suggest an additional feature I'd like to see in Azure Data Factory in the future because some of the features aren't available internally because the features undergo security evaluation first, and my organization controls which features would become available to users.
For how long have I used the solution?
I've been using Azure Data Factory for the last two years.
Buyer's Guide
Azure Data Factory
September 2025

Learn what your peers think about Azure Data Factory. Get advice and tips from experienced pros sharing their opinions. Updated: September 2025.
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What do I think about the stability of the solution?
We're happy with the stability of Azure Data Factory.
What do I think about the scalability of the solution?
Azure Data Factory is scalable, with clusters available on demand. There isn't any issue with scaling the solution.
How are customer service and support?
We have an internal support team and the Azure Data Factory support team. We raise tickets and follow up on those tickets, and on a scale of one to five, we'd rate support as four because sometimes there are delays. Otherwise, we are satisfied with Azure Data Factory support.
How was the initial setup?
My company didn't set up Azure Data Factory as the Azure team did it.
What about the implementation team?
We outsourced the implementation of Azure Data Factory directly to the Azure team.
What's my experience with pricing, setup cost, and licensing?
I have no idea how much Azure Data Factory costs.
Which other solutions did I evaluate?
We're using AWS apart from Azure Data Factory. We're trying out Palantir Foundry as well. They are the leading service providers in the data analytics and ETL world.
What other advice do I have?
I'm familiar with Palantir Foundry, but my company just recently got the Palantir Foundry license, so I'm still not using it, but checking it for shortcomings.
I have experience with Azure Data Factory, too.
I'm unsure of the exact version of Azure Data Factory, but I'm using the latest version or whatever's available on Azure.
I have a vague figure of users of Azure Data Factory, but it's more than one thousand to one thousand five hundred people.
I'd tell people who want to use Azure Data Factory that Microsoft offers excellent courses, ESI (Enterprise Skill Initiatives). You should register and take the courses. Azure Data Factory is a solution I'd recommend to others.
I'd rate Azure Data Factory as nine out of ten because it has a lot of connectors, even custom connectors, for data onboarding. It can also integrate with Spark notebooks and allows my organization to parallelize code. Azure Data Factory also has provisions for Spark and SQL scripts or any scripts, plus the infrastructure is highly scalable, so it's a nine for me.
My organization is a customer of Azure Data Factory.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Microsoft Azure
Disclosure: My company does not have a business relationship with this vendor other than being a customer.

CTO at Sosty
No deployment cost, quick implementation, pay only for the processing time and data
Pros and Cons
- "The solution includes a feature that increases the number of processors used which makes it very powerful and adds to the scalability."
- "The solution can be improved by decreasing the warmup time which currently can take up to five minutes."
What is our primary use case?
The primary use case of this solution is to extract ETLS, transform and load data, and organize database synchronization.
What is most valuable?
The most valuable feature of this solution is the data flow, which is the same SQL server in important service, integration services, which is a very robust and powerful tool to transform data.
What needs improvement?
The solution can be improved by decreasing the warmup time which currently can take up to five minutes.
For how long have I used the solution?
I have been using the solution for two years.
What do I think about the stability of the solution?
The solution is extremely stable.
What do I think about the scalability of the solution?
The solution is scalable.
Which solution did I use previously and why did I switch?
Previously I used AWS Glue and SSIS.
How was the initial setup?
The initial setup is straightforward.
What about the implementation team?
The implementation was completed in-house and is immediate because it is a native cloud tool.
What was our ROI?
I have seen an ROI with the time saved migrating data for reports.
What's my experience with pricing, setup cost, and licensing?
The solution's fees are based on a pay-per-minute use plus the amount of data required to process. The more data you process the more CPUs and time is required which increases the cost of using this solution.
What other advice do I have?
I give the solution ten out of ten.
The only thing you need to deploy the solution is to click on publish.
The solution includes a feature that increases the number of processors used which makes it very powerful and adds to the scalability.
We have three people using the solution in our organization and one engineer that maintains it.
I recommend to any potential user to factor in the five-minute warm-up time that is required for each execution.
Which deployment model are you using for this solution?
Private Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Microsoft Azure
Disclosure: My company does not have a business relationship with this vendor other than being a customer.
Buyer's Guide
Azure Data Factory
September 2025

Learn what your peers think about Azure Data Factory. Get advice and tips from experienced pros sharing their opinions. Updated: September 2025.
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Solution Architect at a computer software company with 1,001-5,000 employees
Helps us to load data to warehouses and useful for ETL processes
Pros and Cons
- "The tool's most valuable features are its connectors. It has many out-of-the-box connectors. We use ADF for ETL processes. Our main use case involves integrating data from various databases, processing it, and loading it into the target database. ADF plays a crucial role in orchestrating these ETL workflows."
- "When working with AWS, we have noticed that the difference between ADF and AWS is that AWS is more customer-focused. They're more responsive compared to any other company. ADF is not as good as AWS, but it should be. If AWS is ten out of ten, ADF is around eight out of ten. I think AWS is easier to understand from the GUI perspective compared to ADF."
What is our primary use case?
We use the product for data warehouses. It helps us to load data to warehouses.
What is most valuable?
The tool's most valuable features are its connectors. It has many out-of-the-box connectors. We use ADF for ETL processes. Our main use case involves integrating data from various databases, processing it, and loading it into the target database. ADF plays a crucial role in orchestrating these ETL workflows.
The tool's visual interface is good. The ADS scheduling feature impacts data management by determining when jobs must be run and setting up dependencies. This capability eliminates the need to rely on enterprise data scheduling tools.
What needs improvement?
When working with AWS, we have noticed that the difference between ADF and AWS is that AWS is more customer-focused. They're more responsive compared to any other company. ADF is not as good as AWS, but it should be. If AWS is ten out of ten, ADF is around eight out of ten. I think AWS is easier to understand from the GUI perspective compared to ADF.
For how long have I used the solution?
I have been using the product for 6 months.
What do I think about the stability of the solution?
ADF is stable.
What do I think about the scalability of the solution?
I rate the tool's scalability an eight out of ten.
How was the initial setup?
The tool's deployment is easy. The deployment typically takes around two to three days to set up. However, the duration may vary depending on factors such as the number of integrated endpoints. In our company, the deployment team had three to four people. This team consisted of an IT engineer, a network engineer, and an ETL admin.
We still haven't required much maintenance since we're still in the development phase. However, as time progresses and we move into production, we'll better understand the maintenance requirements.
What's my experience with pricing, setup cost, and licensing?
ADF is cheaper compared to AWS.
What other advice do I have?
The tool has met our projects' growing data needs effectively so far. I rate it an eight out of ten.
Disclosure: My company does not have a business relationship with this vendor other than being a customer.
Senior Devops Consultant (CPE India Delivery Lead) at a computer software company with 201-500 employees
Useful as an ETL tool for medium to large-sized businesses
Pros and Cons
- "The scalability of the product is impressive."
- "The product's technical support has certain shortcomings, making it an area where improvements are required."
What is our primary use case?
Azure Data Factory is an all-in-one solution for ETL in our company.
My company doesn't use the product for development purposes.
I use the solution in my company as an ETL tool and for orchestration.
What is most valuable?
As a DevOps engineer, I feel that the CI/CD part and the tool's integration with GitHub are the product's best features. If you compare it with other tools, like Glue, AWS, and other solutions, I feel Azure Data Factory's deployment part is a lot easier to manage. The code promotions and the data pipeline promotions to higher environments are a lot easier with Azure Data Factory.
What needs improvement?
The product's technical support has certain shortcomings, making it an area where improvements are required. Instead of sending out documents, I think the tool's support team should focus on how to troubleshoot issues. I want the tool's support team to have real-time interaction with users.
The product's price can be problematic for small businesses, making it an area where improvements are required.
For how long have I used the solution?
I have experience with Azure Data Factory. I am the end user of the tool. Azure Data Factory is a PaaS solution. I use the solution's latest version.
What do I think about the stability of the solution?
It is a stable solution since it is a PaaS product. Stability-wise, I rate the solution an eight out of ten.
What do I think about the scalability of the solution?
The scalability of the product is impressive. Scalability-wise, I rate the solution an eight out of ten.
Most of the people in my company work on Azure, and those who want to use the native ETL capabilities provided by the product opt for Azure Data Factory.
The product is useful in medium to large-sized businesses. Smaller businesses can opt for other options other than Azure Data Factory, considering the amount of money they are ready to spend. There are better options available in the market than Azure Data Factory.
How are customer service and support?
I rate the technical support a five to six out of ten.
How would you rate customer service and support?
Neutral
How was the initial setup?
I rate the product's initial setup phase a seven or eight on a scale of one to ten, where one is difficult and ten is easy.
In my company, we take care of the product's deployment process and maintenance phase.
The solution is deployed using Azure's cloud services.
The solution can be deployed in ten to fifteen minutes.
For deployments, my company usually creates codes in Terraform so that we can have automated deployments, and it is connected to us with a CI/CD tool like Azure DevOps. Azure DevOps does the automated deployment for our company.
During the setup phase, users may face issues when it comes to infrastructure deployment and the configuration around it, especially if you consider the integration runtime, as it is something that is too complicated for a normal developer to understand. There is a need for a cloud expert with a good understanding to be able to take care of the deployment in the right manner and in a secure way. The networking setup and security part of the product are a bit complicated, which I might understand since I am a DevOps engineer, but a developer who is new to the product might not understand such parts of the tool. The deployment of the service in an infrastructure can be possible only if the person involved in the deployment has a basic level of understanding related to the product.
What's my experience with pricing, setup cost, and licensing?
I rate the product price as six on a scale of one to ten, where one is low price and ten is high price.
Which other solutions did I evaluate?
I wanted to compare Azure Data Factory with Fivetran.
What other advice do I have?
Users rely on Azure Data Factory's connectors to meet data integration and transformation needs. Users use connectors that are native to Azure Data Factory. The tool offers more than 90 connectors that can be used to ingest data from different sources.
The feature of the solution I find to be the most beneficial for data management tasks is its connectors, and it can even be used for hybrid scenarios. The tool can connect to a different cloud, like AWS. The product can connect to your on-premises systems. In general, users are able to ingest data from everywhere, and the best part is that all of the aforementioned areas can be managed through GUI. The tool is like a low code-no code solution.
The visual interface of the solution impacts workflow efficiency because I think it is easier to start with for any developer who wants to use the tool. It is easier to start with and also easier to troubleshoot or debug, especially at a time when you cannot expect all your developers to understand codes. It would be good to have an intuitive GUI. Azure Data Factory
does a pretty good job when you compare it with its competitors.
Most of the time, my company uses integration runtime, so we mostly use a self-hosted integration runtime. In short, my company has not seen my impact has not seen much impact on a project from the product's scalability capabilities on any projects because we use it according to the needs of our customers.
I rate the tool an eight out of ten.
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Microsoft Azure
Disclosure: My company has a business relationship with this vendor other than being a customer. reseller
Specialist Software Engineer at a financial services firm with 10,001+ employees
Faster than other solutions, has multiple connectors, and is easy to set up
Pros and Cons
- "One advantage of Azure Data Factory is that it's fast, unlike SSIS and other on-premise tools. It's also very convenient because it has multiple connectors. The availability of native connectors allows you to connect to several resources to analyze data streams."
- "There's no Oracle connector if you want to do transformation using data flow activity, so Azure Data Factory needs more connectors for data flow transformation."
What is our primary use case?
I use Azure Data Factory for architecture creation, for example, loading data from Oracle DB to Azure Synapse Analytics, creating facts and dimensions using Azure Data Pipeline, and creating Azure Synapse notebooks for data transformation.
Another use case for Azure Data Factory is dashboard creation to help customers make informed decisions.
How has it helped my organization?
Compared to the on-premise SSIS, Azure Data Factory has better infrastructure. It also benefits my company because you can scale the solution up or down with different resources.
Azure Data Factory is also on a pay-as-you-go or pay-as-you-use model, which is suitable for the company because my company only pays for its usage or requirement.
The solution is also very user-friendly, and the Azure Data Factory support team responds quickly whenever my team has a loading issue.
What is most valuable?
One advantage of Azure Data Factory is that it's fast, unlike SSIS and other on-premise tools.
It's also very convenient because Azure Data Factory has multiple connectors. It has sixty connectors which you can't find in SSIS. The availability of native connectors allows you to connect to several resources to analyze data streams.
I also like that you can set up your own VM and infrastructure on Azure Data Factory without any help from the IT team because it only requires a single click.
What needs improvement?
What's missing in Azure Data Factory is an Oracle connector. If you want to connect directly to the Oracle database, you must copy and transform the data. There's no Oracle connector if you want to do transformation using data flow activity, so Azure Data Factory needs more connectors for data flow transformation.
Sending out emails after a job is completed is another area for improvement in the tool.
For how long have I used the solution?
I've been using Azure Data Factory for three years.
What do I think about the scalability of the solution?
Azure Data Factory is a scalable tool.
Which solution did I use previously and why did I switch?
We used SSIS, but its on-premise version is slower than Azure Data Factory, and Azure Data Factory, infrastructure-wise, is better, so we went with Azure Data Factory.
How was the initial setup?
The initial setup for Azure Data Factory is an eight out of ten.
Manually deploying Azure Data Factory is easy and doesn't take much time, but I'm not sure how long it takes for an automated approach to deployment.
What's my experience with pricing, setup cost, and licensing?
The licensing model for Azure Data Factory is good because you won't have to overpay. Pricing-wise, the solution is a five out of ten. It was not expensive, and it was not cheap. It's in the middle.
What other advice do I have?
I have experience with both Azure Data Factory and SSIS.
I'm using the latest version of Azure Data Factory.
My rating for Azure Data Factory is eight out of ten.
My company is an Azure Data Factory user.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Microsoft Azure
Disclosure: My company does not have a business relationship with this vendor other than being a customer.
Head of Digital Engineering, Management Consultant at Stax Inc.
Easy to set up, has a pipeline feature and built-in security, and allows you to connect to different sources
Pros and Cons
- "The feature I found most helpful in Azure Data Factory is the pipeline feature, including being able to connect to different sources. Azure Data Factory also has built-in security, which is another valuable feature."
- "Areas for improvement in Azure Data Factory include connectivity and integration. When you use integration runtime, whenever there's a failure, the backup process in Azure Data Factory takes time, so this is another area for improvement."
What is our primary use case?
As a management consultancy company, we help our clients deploy Azure Data Factory or any other cloud-based solution depending on data integration needs. Regarding how we use Azure Data Factory within our company, we are on the Microsoft Stack, so we use the solution primarily for data warehousing and integration.
What is most valuable?
The feature I found most helpful in Azure Data Factory is the pipeline feature, including being able to connect to different sources.
I also found running Python codes whenever you need to valuable in Azure Data Factory, especially for certain features of the solution, such as data integrations, aggregations, and manipulations.
Azure Data Factory also has built-in security, which is another valuable feature.
I also like that you get access to the whole Azure suite through Azure Data Factory, so the overall architecture design, defining security and access, role-based access management, etc. It's helpful to have the whole suite when designing applications.
What needs improvement?
Areas for improvement in Azure Data Factory include connectivity and integration.
When you use integration runtime, whenever there's a failure, the backup process in Azure Data Factory takes time, so this is another area for improvement.
Database support in the solution also has room for improvement because Azure Data Factory only currently supports MS SQL and Postgres. I want to see it supporting other databases.
If you want to connect the solution from on-premises to the cloud, you will have to go with a VPN or a pretty expensive route connection. A VPN connection might not work most of the time because you have to download a client and install it, so an interim solution for secure access from on-premise locations to the cloud is what I want to see in Azure Data Factory.
For how long have I used the solution?
I've been using Azure Data Factory for about a year now.
What do I think about the stability of the solution?
Azure Data Factory is very stable, so it's a four out of five for me. In some instances, the solution failed, but I wouldn't wholly blame Azure Data Factory because my company connected to some on-premise databases in some cases. Sometimes, you'll get errors from self-hosted integration, faulty connections, or the on-premise server is down, so my rating for stability is a four.
What do I think about the scalability of the solution?
Scalability-wise, Azure Data Factory is a four out of five because Microsoft is still developing certain tiers, which means you can't upgrade an older skill or tier. In contrast, the more modern, newer tiers could be upgraded easily. Rarely will you get stuck in one platform where you have completely destroyed that container and then fit a new container. Most of the time, Azure Data Factory is pretty easy to scale.
How are customer service and support?
We haven't used Microsoft support directly because whenever we have issues with Azure Data Factory, we can find resolutions through their online documentation.
Which solution did I use previously and why did I switch?
We're using both Azure Data Factory and SSIS.
We had several in-house solutions, but we moved to Azure Data Factory because it was straightforward. From a deployment standpoint, the solution comes with different services, so we didn't have to worry about separate hardware or infrastructure for networking, security, etc.
How was the initial setup?
The initial setup for Azure Data Factory was easy, so I'd rate the setup a four out of five.
The implementation strategy was looking into what my organization needed overall, then planning and direct deployment. My company first did a test, a dummy version, then a UAT with stakeholders before going into production.
It took about two months to complete the deployment for Azure Data Factory.
What about the implementation team?
An in-house team, the digital data engineering team, deployed Azure Data Factory.
What was our ROI?
We're still computing the ROI from Azure Data Factory. It's too early to comment on that.
What's my experience with pricing, setup cost, and licensing?
My company is on a monthly subscription for Azure Data Factory, but it's more of a pay-as-you-go model where your monthly invoice depends on how many resources you use.
On a scale of one to five, pricing for Azure Data Factory is a four.
It's just the usage fees my company pays monthly. No support fees because my company didn't need support from Microsoft.
If you're not using core Microsoft products, the cost could be slightly higher, for example, when using a Postgres database versus an MS SQL database.
What other advice do I have?
My company uses Azure Data Factory, SSIS, and for a few other instances, Salesforce.
My company currently has about fifty Azure Data Factory users, but not directly exposed to the solution compared to the developers; for example, members of corporate management and other teams apart from the development team are exposed to Azure Data Factory.
Shortly, there could be about two hundred users of Azure Data Factory within the company.
The developer team working directly on Azure Data Factory comprises ten individuals.
For the maintenance of the solution, my company has two to three staff, but it could reach up to eight or ten for the entire product. It's a mix of engineers and business analysts who handle Azure Data Factory maintenance.
I'd rate Azure Data Factory as eight out of ten.
My company is an end user of Azure.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Microsoft Azure
Disclosure: My company does not have a business relationship with this vendor other than being a customer.
Data Architect at World Vision
The good, the bad and the lots of ugly
Pros and Cons
- "The trigger scheduling options are decently robust."
- "There is no built-in pipeline exit activity when encountering an error."
What is our primary use case?
The current use is for extracting data from Google Analytics into Azure SQL Database as a source for our EDW. Extracting from GA was problematic with SSIS.
The larger use case is to assess the viability of the tool for larger use in our organization as a replacement for SSIS for our EDW and also as an orchestration agent to replace SQL Agent for firing SSIS packages using Azure SSIS-IR.
The initial rollout was to solve the immediate problem while assessing its ability to be used for other purposes within the organization. And also establish the development and administration pipeline process.
How has it helped my organization?
ADF allowed us to extract Google Analytics data (via BigQuery) without purchasing an adapter.
It has also helped with establishing how our team can operate within Azure using both PaaS and IaaS resources and how those can interact. Rolling out a small data factory has forced us to understand more about all of Azure and how ADF needs to rely upon and interact with other Azure resources.
It provides a learning ground for use of DevOps Git along with managing ARM templates as well as driving the need to establish best practices for CI.
What is most valuable?
The most valuable aspect has been a large list of no-cost source and target adapters.
It is also providing a PaaS ELT solution that integrates with other Azure resources.
Its graphical UI is very good and is even now improving significantly with the latest preview feature of displaying inner activities within other activities such as forEach and If conditions.
Its built-in monitoring and ability to see each activity's JSON inputs/outputs provide an excellent audit trail.
The trigger scheduling options are decently robust.
The fact that it's continually evolving is hopeful that even if some feature is missing today, it may be soon resolved. For example, it lacked support for simple SQL activity until earlier this year, when that was resolved. They have now added a "debug until" option for all activities. The Copy Activity Upsert option did not perform well at all when I first started using the tool but now seems to have acceptable performance.
The tool is designed to be metadata driven for large numbers of patterned ETL processes, similar to what BIML is commonly used for in SSIS but much simpler to use than BIML. BIML now supports generating ADF code although with ADF's capabilities I'm not sure BIML still holds its same value as it did for SSIS.
What needs improvement?
The list of issues and gaps in this tool is extensive, although as time goes on, it gets shorter. It currently includes:
1) Missing email/SMTP activity
2) Mapping data flows requires significant lag time to spin up spark clusters
3) Performance compared to SSIS. Expect copy activity to take ten times that of what SSIS takes for simple data flow between tables in the same database
4) It is missing the debug of a single activity. The workaround is setting a breakpoint on the task and doing a "rerun from activity" or setting debug on activity and running up to that point
5) OAuth 2.0 adapters lack automated support for refresh tokens
6) Copy activity errors provide no guidance as to which column is causing a failure
7) There's no built-in pipeline exit activity when encountering an error
8) Auto Resolve Integration runtime should never pick a region that you're not using (should be your default for your tenant)
9) IR (integration runtime) queue time lag. For example, a small table copy activity I just ran took 95 seconds of queuing and 12 seconds to actually copy the data. Often the queuing time greatly exceeds the actual runtime
10) Activity dependencies are always AND (OR not supported). This is a significant missing capability that forces unnecessary complex workarounds just to handle OR situations when they could just enhance the dependency to support OR like SSIS does. Did I just ask when ADF will be as good as SSIS?
They need to fix bugs. For example:
1) The debug sometimes stops picking up saved changes for a period of time, rendering this essential tool useless during that time
2) Enable interactive authoring (a critical tool for development) often doesn't turn on when enabled without going into another part of the tool to enable it. Then, you have to wait several minutes before it's enabled which is time you're blocked from development until it's ready. And then it only activates for up to 120 minutes before you have to go through this all over again. I think Microsoft is trying to torture developers
3) Exiting the inside of an activity that contains other activities always causes the screen to jump to the beginning of a pipeline requiring re-navigating where you were at (greatly slowing development productivity)
4) Auto Resolve Integration runtime (using default settings) often picks remote regions (not necessarily even paired regions!) to operate, which causes either an unnecessary slowdown or an error message saying it's unable to transfer the volume of data across regions
5) Copy activity often gets the error "mapping source is empty" for no apparent reason. If you play with the activity such as importing new metadata then it's happy again. This sort of thing makes you want to just change careers. Or tools.
For how long have I used the solution?
I have been using this product for six months.
What do I think about the stability of the solution?
Production operation seems to run reliably so far, however, the development environment seems very buggy where something works one day and not the next.
What do I think about the scalability of the solution?
So far, the performance of this solution is abysmal compared to SSIS. Especially with small tasks such as copying activity from one table to another within the same database.
How are customer service and support?
Customer support is non-existent. I logged multiple issues only to hear back from 1st level support weeks later asking questions and providing no help other than wasting my time. In one situation it was a bug where the debug function stopped working for a couple of days. By the time they got back to me, the problem went away.
How would you rate customer service and support?
Negative
Which solution did I use previously and why did I switch?
We have been and still rely on SSIS for our ETL. ADF seems to do ELT well but I would not consider it for use in ETL at this time. Its mapping data flows are too slow (which is a large understatement) to be of practical use to us. Also, the ARM template situation is impractical for hundreds of pipelines like we would have if we converted all our SSIS packages into pipelines as a single ADF couldn't take on all our pipelines.
How was the initial setup?
Initial setup is the largest caveat for this tool. Once you've organized your Azure environment and set up DevOps pipelines, the rest is a breeze. But this is NOT a trivial step if you're the first one to establish the use of ADF at your organization or within your subscription(s). Instead of learning just an ETL tool, you have to get familiar with and establish best practices for the entire Azure and DevOps technologies. That's a lot to take on just to get some data movements operational.
What about the implementation team?
I did this in-house with the assistance of another team who uses DevOps with Azure for other purposes (non-ADF use).
What's my experience with pricing, setup cost, and licensing?
The setup cost is only the time it takes to organize Azure resources so you can operate effectively and figure out how to manage different environments (dev/test/sit/UAT/prod, etc.). Also, how to enable multiple developers to work on a single data factory without losing changes or conflicting with other changes.
Which other solutions did I evaluate?
We operate only with SSIS today, and it works very well for us. However, looking toward the future, we will need to eventually find a PaaS solution that will have longer sustainability.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Microsoft Azure
Disclosure: My company does not have a business relationship with this vendor other than being a customer.
Integration Solutions Lead | Digital Core Transformation Service Line at Hexaware Technologies Limited
Helps to pull records and parse them quickly, but the exception handling and logging mechanisms can be improved
Pros and Cons
- "We have found the bulk load feature very valuable."
- "When the record fails, it's tough to identify and log."
What is our primary use case?
Our primary use case for the solution is data integration and we deploy it only on Azure.
How has it helped my organization?
When we were integrating the Ports product with our internal data warehouse, we had to update all the reports to our internal data warehouse on the Ports system database. However, they were not given access to the database company, and they dump some files or provide you with them. In one case, they were providing files. In another case, they provided some APIs where you need to call in a batch of thousands of records multiple times. It works very well with Azure Data Factory to pull the records, parse them quickly and post them in the database and data warehouse.
What is most valuable?
We have found the bulk load feature very valuable.
What needs improvement?
The only challenge with Azure Data Factory is its exception-handling mechanism. When the record fails, it's tough to identify and log.
For how long have I used the solution?
We have been using the solution for a year and a half and are currently using the latest version.
What do I think about the scalability of the solution?
The solution is scalable and we intend to further increase its usage in the future.
How are customer service and support?
I rate customer service and support an eight out of ten.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
We previously used different solutions.
How was the initial setup?
The initial setup is straightforward.
What about the implementation team?
The implementation was done in-house.
What's my experience with pricing, setup cost, and licensing?
I cannot comment on licensing costs because I was not involved.
What other advice do I have?
I rate the solution a six out of ten. The solution is good but its exception handling and logging mechanisms can be improved. I advice users considering this solution to go for it especially if their integrations are heavy on the data side.
Disclosure: My company does not have a business relationship with this vendor other than being a customer.

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Updated: September 2025
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Download our free Azure Data Factory Report and get advice and tips from experienced pros
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