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Azure Data Factory OverviewUNIXBusinessApplication

Azure Data Factory is #2 ranked solution in top Data Integration Tools and #3 ranked solution in top Cloud Data Warehouse tools. PeerSpot users give Azure Data Factory an average rating of 8 out of 10. Azure Data Factory is most commonly compared to Informatica PowerCenter: Azure Data Factory vs Informatica PowerCenter. Azure Data Factory is popular among the large enterprise segment, accounting for 68% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a computer software company, accounting for 28% of all views.
What is Azure Data Factory?

Create, schedule, and manage your data integration at scale with Azure Data Factory - a hybrid data integration (ETL) service. Work with data wherever it lives, in the cloud or on-premises, with enterprise-grade security.

Azure Data Factory Buyer's Guide

Download the Azure Data Factory Buyer's Guide including reviews and more. Updated: May 2022

Azure Data Factory Customers

Milliman, Pier 1 Imports, Rockwell Automation, Ziosk, Real Madrid

Azure Data Factory Video

Azure Data Factory Pricing Advice

What users are saying about Azure Data Factory pricing:
  • "Pricing appears to be reasonable in my opinion."
  • "I would not say that this product is overly expensive."
  • "The price you pay is determined by how much you use it."
  • Azure Data Factory Reviews

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    Richard Domikis - PeerSpot reviewer
    Chief Technology Officer at cornerstone defense
    Real User
    Top 5Leaderboard
    Easy to bring in outside capabilities, flexible, and works well
    Pros and Cons
    • "It is very modular. It works well. We've used Data Factory and then made calls to libraries outside of Data Factory to do things that it wasn't optimized to do, and it worked really well. It is obviously proprietary in regards to Microsoft created it, but it is pretty easy and direct to bring in outside capabilities into Data Factory."
    • "There is always room to improve. There should be good examples of use that, of course, customers aren't always willing to share. It is Catch-22. It would help the user base if everybody had really good examples of deployments that worked, but when you ask people to put out their good deployments, which also includes me, you usually got, "No, I'm not going to do that." They don't have enough good examples. Microsoft probably just needs to pay one of their partners to build 20 or 30 examples of functional Data Factories and then share them as a user base."

    What is our primary use case?

    Our customers use it for data analytics on a large volume of data. So, they're basically bringing data in from multiple sources, and they are doing ETL extraction, transformation, and loading. Then they do initial analytics, populate a data lake, and after that, they take the data from the data lake into more on-premise complex analytics. Its version depends on a customer's environment. Sometimes, we use the latest version, and sometimes, we use the previous versions.

    What is most valuable?

    It is very modular. It works well. We've used Data Factory and then made calls to libraries outside of Data Factory to do things that it wasn't optimized to do, and it worked really well. It is obviously proprietary in regards to Microsoft created it, but it is pretty easy and direct to bring in outside capabilities into Data Factory. It is very flexible. You can build any features you want.

    What needs improvement?

    There is always room to improve. There should be good examples of use that, of course, customers aren't always willing to share. It is Catch-22. It would help the user base if everybody had really good examples of deployments that worked, but when you ask people to put out their good deployments, which also includes me, you usually got, "No, I'm not going to do that." They don't have enough good examples. Microsoft probably just needs to pay one of their partners to build 20 or 30 examples of functional Data Factories and then share them as a user base.

    For how long have I used the solution?

    I have been using this solution for the last five years, but probably, the last three years have been significant.
    Buyer's Guide
    Azure Data Factory
    May 2022
    Learn what your peers think about Azure Data Factory. Get advice and tips from experienced pros sharing their opinions. Updated: May 2022.
    599,220 professionals have used our research since 2012.

    What do I think about the stability of the solution?

    It has been stable. I have not experienced any issues.

    What do I think about the scalability of the solution?

    It is decent for most things. I'm not sure if it is necessarily intended for large volume and high-speed streams of data. By large, I mean really big, but for pretty much anything that most users would want to do, including ourselves, it is fine. Our clients are large government organizations. It scales fine within its environment. You can literally throw another Data Factory in or replicate one and do things pretty quickly. So, it is not at all hard to increase your processing footprint, but you have to pay for it. It doesn't end up being quite expensive. Although I haven't really done it, I would suspect that if I did the equivalent in AWS, Azure would be more expensive than AWS because of the way they price data.

    How are customer service and support?

    They're all right. I would rate them a seven out of 10. They do fine, but there is a lot that they don't do. I'm not sure if even Microsoft has enough SMEs from a user point of view. They are helpful for getting it set up, making it work, and helping you figure out why it doesn't work. If you want to ask them about something that you are trying to do, they'll try to direct you to a partner, which is fine, but the partners also don't necessarily have an experience. It is Catch-22. There aren't a lot of people out there with Azure experience because Azure started to be in demand only over the last two years.

    Which solution did I use previously and why did I switch?

    The customer used a lot of homebrew stuff. They were doing a lot of internal stuff and some Oracle stuff. They were doing things, and they made a workaround and said, "Okay, we'll bring it into Oracle Database, and then we'll do all these things to it." We're like, "Okay, that works, but then you're taking it out of that database and putting it over into the data lake. I don't understand why are you doing that?" That's what they were doing.

    How was the initial setup?

    It is pretty straightforward. Devil is in the details, but you can easily get up and running in a day with Data Factory. Anybody who is comfortable in Azure can set up Data Factory, but it takes experience to know what it can and can't do or should and shouldn't do.

    What other advice do I have?

    It is proven, and it works. Make sure you have a well-defined use case and build a quick prototype to ensure that it, in fact, does what you need. Give yourself some benchmarks. That's exactly what we did. We defined the use case, and then we set up Data Factory. We found a couple of things that it didn't do. We figured out a way to work around those things and have it do those things. After that, we confirmed it. It is operational, and it is doing its job. It has been pretty much error-free since then. It would become easier to use as more people become Azure-capable. If I want to find an AWS SME, I can get tons. They're expensive, but I have them. If I want to find an Azure SME, I usually have to create them. Azure was later to market than AWS. So, there are fewer people who are experts in Azure, and they are in high demand. I would rate Azure Data Factory a nine out of 10. They just don't have enough good examples out there of things.

    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 has a business relationship with this vendor other than being a customer: Partner
    Senior Manager at a tech services company with 51-200 employees
    Real User
    Top 20
    Reasonably priced, scales well, good performance
    Pros and Cons
    • "The solution can scale very easily."
    • "My only problem is the seamless connectivity with various other databases, for example, SAP."

    What is our primary use case?

    My primary use case is getting data from the sensors.

    The sensors are installed on the various equipment across the plant, and this sensor gives us a huge amount of data. Some are captured on a millisecond basis.

    What we are able to do is the data into Azure Data Factory, and it has allowed us to scale up well. We are able to utilize that data for our predictive maintenance of the assets of the equipment, as well as the prediction of the breakdown. Specifically, we use the data to look at predictions for future possible breakdowns. At least, that is what we are looking to build towards.

    How has it helped my organization?

    It has helped us to take care of a lot of our analytics requirements. We are running a few analytics models on Data Factory, which is very helpful.

    What is most valuable?

    The overall architecture has been very valuable to us. It has allowed us to scale up pretty rapidly. That's something that has been very good for us. 

    The solution can scale very easily.

    The stability is very good and has improved very much over time.

    What needs improvement?

    My only problem is the seamless connectivity with various other databases, for example, SAP. Our transaction data there, all the maintenance data, is maintained in SAP. That seamless connectivity is not there. 

    Basically, it could have some specific APIs that allow it to connect to the traditional ERP systems. That'll make it more powerful. With Oracle, it's pretty good at this already. However, when it comes to SAP, SAP has its native applications, which are the way it is written. It's very much AWS with SAP Cloud, so when it comes to Azure, it's difficult to fetch data from SAP.

    The initial setup is a bit complex. It's likely a company may need to enlist assistance.

    Technical support is lacking in terms of responsiveness.

    For how long have I used the solution?

    We've been using the solution roughly for about a year and a half.

    It hasn't been an extremely long amount of time. 

    What do I think about the stability of the solution?

    From a security perspective, the product has come up a long way.

    With the Azure Cloud Platform, in 2015, I was in a different organization and it was not reliable at all. It has become much more reliable since then and is very stable at the moment. It's reliable.

    What do I think about the scalability of the solution?

    The solution is pretty easy to scale on Azure. I have found it to be very efficient and it is pretty fast. You just need to get the order done properly, and then you will be able to scale up.

    We have about five to seven people using it at this time.

    How are customer service and technical support?

    Technical support isn't the best, as it's a bit delayed at times.

    Whenever we need some urgent support, wherein we have to restart or something has stuck, it takes a bit of time. Some improvements can be made in the customer support area.

    In summary, we are not completely satisfied with the support.

    How was the initial setup?

    The initial setup is not straightforward. It's a bit complex. A company may need to hire someone to assist them with the process.

    The solution's deployment took about eight weeks.

    What about the implementation team?

    I had to hire technical experts who could help us in the process. We could not handle the implementation ourselves.

    What's my experience with pricing, setup cost, and licensing?

    Cost-wise, it is quite affordable. It's not a factor in the decision-making process when it comes to whether or not we should use it. That said, the pricing is very reasonable.

    Which other solutions did I evaluate?

    We evaluated both Oracle and SAP before choosing Azure Data Factory.

    What other advice do I have?

    We are customers and end-users.

    I'd advise companies considering the solution that they need to be very clear about the use case they are trying to address. They need to understand the data ecosystem that they have and what percentage of data is coming in from the various ERP systems.

    Do that study properly and then come up with the right solution. If, for example, it is that the underlying data that they want to analyze is more than 60% residing in SAP, then probably Azure would not be the right platform to move ahead with.

    We're mostly satisfied with the product. However, getting it connected to closed ERP systems like SAP would make it more powerful.

    I would rate the solution eight out of ten.

    Which deployment model are you using for this solution?

    Private Cloud
    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    Buyer's Guide
    Azure Data Factory
    May 2022
    Learn what your peers think about Azure Data Factory. Get advice and tips from experienced pros sharing their opinions. Updated: May 2022.
    599,220 professionals have used our research since 2012.
    Kamlesh Sancheti - PeerSpot reviewer
    Director at a tech services company with 1-10 employees
    Real User
    Top 10Leaderboard
    Comprehensive and user-friendly
    Pros and Cons
    • "Azure Data Factory's most valuable features are the packages and the data transformation that it allows us to do, which is more drag and drop, or a visual interface. So, that eases the entire process."
    • "We are too early into the entire cycle for us to really comment on what problems we face. We're mostly using it for transformations, like ETL tasks. I think we are comfortable with the facts or the facts setting. But for other parts, it is too early to comment on."

    What is our primary use case?

    Azure Data Factory is for data transformation and data loading. It works from your transaction systems, and we are using it for our HRMS, Human Resource Capital Management System. It picks up all the transactional data pick and moves into the Azure Data Warehouse. From there, we would like to create reports in terms of our financial positions and our resource utilization project. These are the reports that we need to build onto the warehouse.

    The purpose of Azure Data Factory is more about transformations, so it doesn't need to have a good dashboard. But, it has a feeding user interface for us to do our activities and debug actions. I think that's good enough.

    What is most valuable?

    Azure Data Factory's most valuable features are the packages and the data transformation that it allows us to do, which is more drag and drop, or a visual interface. So, that eases the entire process.

    Azure Data Factory setup is quite user-friendly.

    I am happy with the interface.

    What needs improvement?

    We are too early into the entire cycle for us to really comment on what problems we face. We're mostly using it for transformations, like ETL tasks. I think we are comfortable with the facts or the facts setting. But for other parts, it is too early to comment on.

    We are still in the development phase, testing it on a very small set of data, maybe then the neatest four or bigger set of data. Then, you might get some pain points once we put it in place and run it. That's when it will be more effective for me to answer that.

    For how long have I used the solution?

    We are building Azure Data Factory right now internally to extract data from our transactional systems and put them into the warehouse so that the reporting engine can be built too.

    What do I think about the scalability of the solution?

    We have not tried it scaling up. But, Azure promises the stability and scalability should not be an issue.

    From a development perspective, I think there were four developers who use Azure Data Factory. From a warehouse perspective, once we roll out the reports out, it should be used by at least 40 or 50 people minimum.

    How are customer service and technical support?

    Generally, the documentation is pretty decent. All the issues that come up are here in the documentation part. We've not really had to go to Microsoft as of now from a support perspective. The documentation and the support that we get over the internet is quite good.

    How was the initial setup?

    The initial setup was very straightforward.

    The initial setup was quite quick, nothing much to do. Now, we are more developing the use cases. A use case with data generally takes around four or five days a use case because it will start right from identifying the right field, getting the data, transforming it, and finalizing the warehouse structure. That makes a bit of a thing, but it's pretty straightforward.

    What about the implementation team?

    We are a technical team so we implemented it in-house.

    What's my experience with pricing, setup cost, and licensing?

    It's a pay-as-you-go module. I'm not very sure about cost because our usage currently is very low. But, I feel that if the usage extends beyond a certain threshold, it will start getting expensive.

    It depends what the threshold is. I see we're not at that threshold right now, so it's pretty decent right now.

    Which other solutions did I evaluate?

    We were looking at certain other projects and products. For example, we were looking at Snowflake that has a data warehouse. But the project wasn't working. That's why we selected Azure. The primary reason is the skills are very easily available for Azure. The second is from our strategy perspective, because we were trying to be a Microsoft shop it fits into our strategy. That's all.

    What other advice do I have?

    If you're a Microsoft shop, if you want to get there easily, I think Azure is one of the better choices. Otherwise, other tools generally require specialized skills and specialized partners to come and implement it. Once implemented, then it becomes much easier to install.

    I can't comment right now. I've not talked to it in that fashion. Whatever was required by us, business users have been satisfied in the Data Factory setup.

    On a scale of one to ten, I would give Azure Data Factory an eight.

    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: I am a real user, and this review is based on my own experience and opinions.
    Data Analytics Specialist at a pharma/biotech company with 10,001+ employees
    Real User
    Top 5Leaderboard
    Quick delivery due to drag-and-drop interface
    Pros and Cons
    • "One of the most valuable features of Azure Data Factory is the drag-and-drop interface. This helps with workflow management because we can just drag any tables or data sources we need. Because of how easy it is to drag and drop, we can deliver things very quickly. It's more customizable through visual effect."
    • "Data Factory could be improved by eliminating the need for a physical data area. We have to extract data using Data Factory, then create a staging database for it with Azure SQL, which is very, very expensive. Another improvement would be lowering the licensing cost."

    What is our primary use case?

    My primary use case of Azure Data Factory is supporting the data migration for advanced analytics projects. 

    What is most valuable?

    One of the most valuable features of Azure Data Factory is the drag-and-drop interface. This helps with workflow management because we can just drag any tables or data sources we need. Because of how easy it is to drag and drop, we can deliver things very quickly. It's more customizable through visual effect. 

    What needs improvement?

    Data Factory could be improved by eliminating the need for a physical data area. We have to extract data using Data Factory, then create a staging database for it with Azure SQL, which is very, very expensive. Another improvement would be lowering the licensing cost. 

    For how long have I used the solution?

    I have been using this solution for the past year. 

    What do I think about the stability of the solution?

    This solution is stable. We are using an Azure subscription, so there is no maintenance or direct updates, it's just always the latest version.

    What do I think about the scalability of the solution?

    This solution is automatically scalable, since it's in the cloud. At my company, there were more than one thousand people using this solution because we were a big, media-based company. If there are many user requests in the front end application and the system is not responding much or has slow performance, the system will automatically scale up the performance hardware requirements. 

    How are customer service and support?

    I have contacted technical support. I have never faced an issue like that with Denodo. Fortunately, we got some kind of a tutorial PDF, which helps us to deploy everything quickly. 

    Which solution did I use previously and why did I switch?

    Before working with Azure, I worked with Python. In the culture I was working in, there was no integration. We were using Pure Python scripting and Python data manipulation tools. For example, we used Python's pandas library, which we coded to transform and orchestrate the data, which is necessary for the endpoint. It was not at all a visual tool. It took more time than Denodo. 

    How was the initial setup?

    There is no installation because it's on the cloud. You just log on to the cloud with your subscription credentials, then you can use Data Factory directly. 

    What about the implementation team?

    I implemented through an in-house team. 

    What's my experience with pricing, setup cost, and licensing?

    Data Factory is very expensive. We are using an Azure subscription, so Data Factory has no direct updates, it's just always the latest version. Compared to Denodo, Azure is very costly. Azure Framework has multiple services, not only Data Factory. So in the cloud-based solution, if you're selecting a particular service, like Data Factory, you need to pay for each request.

    Which other solutions did I evaluate?

    I also use Denodo. Data Factory is like a transformation layer, but we need an additional staging database or a data storage facility, which is very expensive compared to implementing Denodo. So we extracted the data using Data Factory, then created a staging database with Azure SQL, which cost a huge amount since it's a physical data area. In Denodo, we just implement a layer, which is all handled in Denodo, and not a physical storage mechanism. I prefer customizable data solutions because they improve performance, creativity, and are helpful for front end people.

    In comparison to Data Factory's drag-and-drop interface, Denodo developers need to create all the unified views by coding, so we have to create SQL queries to execute. With Data Factory, you can quickly drag and drop data or tables, but in Denodo, it takes more time because you need to code and test and all that.

    What other advice do I have?

    I rate Data Factory an eight out of ten, mainly because you need a staging database. I recommend Azure to others, but it depends on architecture. In Data Factory, there is no virtualization environment, no layer of virtualization to help integration and doing caching mechanisms. Though Data Factory is there, Denodo is going further. 

    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: I am a real user, and this review is based on my own experience and opinions.
    Flag as inappropriate
    Manoj Kukreja - PeerSpot reviewer
    Technical Director, Senior Cloud Solutions Architect (Big Data Engineering & Data Science) at NorthBay Solutions
    Real User
    Top 20
    Great for gathering data and pipeline orchestration; much improved monitoring feature
    Pros and Cons
    • "An excellent tool for pipeline orchestration."
    • "The solution needs to be more connectable to its own services."

    What is our primary use case?

    We generally implement this product for data transformation for our clients. We create the pipelines and provide training before handing it over to them. We generally deal with large-scale organizations. I'm a senior solutions architect. 

    How has it helped my organization?

    I think the main benefit of this solution is the ease of use, especially for companies that have come from an SSIS type of background where they are used to Microsoft tools. 

    What is most valuable?

    If you have a very simple pipeline you can use Data Factory for transformations, but it's really for serious analytics. This is an excellent tool for pipeline orchestration; connecting the different components and activities as well as gathering data. It's an orchestration tool, not a transformation tool. The monitoring feature has drastically improved.

    What needs improvement?

    Data Factory is embedded in the new Synapse Analytics. The problem is if you're using the core Data Factory, you can't call a notebook within Synapse. It's possible to call Databricks from Data Factory, but not the Spark notebook and I don't understand the reason for that restriction. To my mind, the solution needs to be more connectable to its own services.

    There is a list of features I'd like to see in the next release, most of them related to oversight and security. AWS has a lake builder, which basically enforces the whole oversight concept from the start of your pipeline but unfortunately Microsoft hasn't yet implemented a similar feature.

    For how long have I used the solution?

    I've been using this solution for five years. 

    What do I think about the stability of the solution?

    From what I've seen this is a stable solution. 

    What do I think about the scalability of the solution?

    The solution is easy to scale keeping in mind that Data Factory doesn't do any computations. We use it mainly to push the computations to Databricks or Synapse. Projects with our clients generally last a few months and only until they go into production. I believe the ability to increase is always there.

    How are customer service and support?

    We typically do not use customer support, but there were a few cases several years ago as the product was moving to the cloud that things were not so stable and we contacted support services - they were very good. 

    Which solution did I use previously and why did I switch?

    When I first started in this field, everything was basically Hadoop on-premise and Hadoop infrastructure. With the increase in cloud integrations, things have changed. Once the big data services got introduced, we were probably one of the few companies in North America that were actually into analytics and big data and we were the first to implement related Microsoft products in Canada.

    How was the initial setup?

    The initial setup is straightforward. I'm a huge fan and user of CI/CD pipelines and never do deployments manually. It's all automated and deployment takes a few minutes.

    What's my experience with pricing, setup cost, and licensing?

    Licensing costs of Data Factory are reasonable. The cost is mainly on the Synapse and Databricks side of things because they are the tools where the computations are done and where you need more nodes and servers.

    What other advice do I have?

    It's important to study the solution before purchasing it. The problem in this market is that because most users are generally not very knowledgeable, they typically fall for services that are not compatible with their use case. Data Factory comes with all the transformations but that doesn't work for serious analytics customers who generally need to resort to Databricks or Synapse which involves training and education. Since it's a new field and everything has just blasted off, it's very hard for people to catch on.

    In my opinion, Airflow still ranks as number one but I would rate Data Factory an eight out of 10. 

    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?

    Amazon Web Services (AWS)
    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    Flag as inappropriate
    User with 5,001-10,000 employees
    Real User
    Easy to set up, and reasonably priced, but the user experience could be improved
    Pros and Cons
    • "Microsoft supported us when we planned to provision Azure Data Factory over a private link. As a result, we received excellent support from Microsoft."
    • "User-friendliness and user effectiveness are unquestionably important, and it may be a good option here to improve the user experience. However, I believe that more and more sophisticated monitoring would be beneficial."

    What is most valuable?

    Essentially, Azure Data Factory is more aligned to ETL, but I wanted to provide a solution for a full data lake solution where I could leverage functionality, whether it is ETL, data ingestion, data warehousing, or data lake.

    What needs improvement?

    I was planning to switch to Synapse and was just looking into Synapse options.

    I wanted to plug things in and then put them into Power BI. Basically, I'm planning to shift some data, leveraging the skills I wanted to use Synapse for performance.

    I am not a frequent user, and I am not an Azure Data Factory engineer or data engineer. I work as an enterprise architect. Data Factory, in essence, becomes a component of my solution. I see the fitment and plan on using it. It could be Azure Data Factory or Data Lake, but I'm not sure what enhancements it would require.

    User-friendliness and user effectiveness are unquestionably important, and it may be a good option here to improve the user experience. However, I believe that more and more sophisticated monitoring would be beneficial.

    For how long have I used the solution?

    I work as an enterprise architect, and I have been using Azure Data Factory for more than a year.

    I am working with the latest version.

    What do I think about the stability of the solution?

    Azure Data Factory is a stable solution.

    What do I think about the scalability of the solution?

    Azure Data Factory is a scalable product.

    In my current company, I have a team of five people, but in my previous organization, there were 20.

    How are customer service and support?

    Technical support is good. We encountered no technical difficulties. Microsoft supported us when we planned to provision Azure Data Factory over a private link. As a result, we received excellent support from Microsoft.

    Which solution did I use previously and why did I switch?

    Products such as Azure Data Factory and Informatica Enterprise Data Catalog were evaluated. This is something I'm working on. I work as an enterprise architect, so these are the tools that I frequently use.

    Previously, I worked with SSIS. We did not change. Because we were building a cloud-based ETF solution Azure Data Factory was an option, but when it came to on-premises solutions, the SQL server integrating the SSIS tool was one option.

    How was the initial setup?

    The initial setup is easy.

    It took three to four weeks to get up to speed and get comfortable using it.

    What's my experience with pricing, setup cost, and licensing?

    Pricing appears to be reasonable in my opinion.

    What other advice do I have?

    My only advice is that Azure Data Factory, particularly for data ingestion, is a good choice. But if you want to go further and build an entire data lake solution, I believe Synapse, is preferred. In fact, Microsoft is developing and designing it in such a way that, it's an entirely clubbing of data ingestion, and data lake, for all things. They must make a decision: is the solution dedicated to only doing that type of data ingestion, in which case I believe Data Factory is the best option.

    I would have preferred, but I'm not a frequent user there right now. I need to think beyond Data Factory as an open-source project to include machines and everything else. As a result, as previously stated, Data Factory becomes very small at the enterprise architect level. I was inundated with power automation, power ops, power virtualizations, and everything else in Microsoft that I had to think about.

    I would rate Azure Data Factory a seven out of ten.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    Flag as inappropriate
    Anand Kumar Singh - PeerSpot reviewer
    Enterprise Architect at TechnipEnergies
    Real User
    Top 5
    Feature-rich, scales well, and it provides good extract, transform, and load functionality
    Pros and Cons
    • "The best part of this product is the extraction, transformation, and load."
    • "The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."

    What is our primary use case?

    We are not using this product specifically as a data factory. We have taken Synapse Analytics as the entire component for the data warehousing solution. Azure Data Factory is one of the components of that, and we are using it for ETL. 

    How has it helped my organization?

    Prior to this, we did not have a proper data warehousing solution. Instead, we had segregation between different tools like Oracle Data Warehouse, Exadata, and other products. Now, most of the tools that we have are from Microsoft, including Power BI, which has been rolled out throughout the organization. Synapse was the better choice for us to implement, as it has a lot of out-of-the-box connectors that we can utilize for data transformation and organization. 

    What is most valuable?

    The best part of this product is the extraction, transformation, and load. In fact, we have found that the three of them work quite well. We are implementing the cloud-based system right now.

    We see a lot of improvement with the most recent version of this solution. Some of the new features are very important to us.

    What needs improvement?

    The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others.

    For how long have I used the solution?

    We have been working with Azure Data Factory for approximately six months. We are still implementing and it is not live, yet, but we expect it to be in 2021.

    What do I think about the stability of the solution?

    I have found it to be quite stable. Here and there, there could be some issues and problems but overall, I'm okay with the product.

    What do I think about the scalability of the solution?

    Scalability is one of the points that we were looking for because we are hosting approximately two terabytes of data and we expect that it will grow at least five times over the next two years. This is one of the reasons that we adopted this solution.

    In perhaps a year, we will increase our usage.

    How are customer service and technical support?

    The technical support from Microsoft is quite good. if you get good resources and they can provide you with free consulting, then it is quite good. However, when you purchase paid consulting and dedicated support, it is quite costly compared to the market.

    How was the initial setup?

    I don't think that the initial setup was very complex. We have quite an advanced IT infrastructure team and the Microsoft FastTrack team also helped us a lot during the programming of the development and setup.

    What's my experience with pricing, setup cost, and licensing?

    I would not say that this product is overly expensive. It is competing with the other providers, and they have almost the same pricing model.

    What other advice do I have?

    In general, I would recommend this product. However, it depends on the target IT ecosystem. If they are utilizing a lot of Microsoft products like Power BI, Office, Project, SharePoint, and so forth, it's better to implement Data Factory because it will reduce a lot of effort spent to consume data from other sources.

    At this point, I can only rate based on my pre-implementation experience, so I would rate this solution an eight out of ten.

    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: I am a real user, and this review is based on my own experience and opinions.
    Business Unit Manager Data Migration and Integration at a tech services company with 201-500 employees
    Real User
    Top 20
    Offers good flexibility and has good support
    Pros and Cons
    • "The flexibility that Azure Data Factory offers is great."
    • "The number of standard adaptors could be extended further."

    What is our primary use case?

    We use this solution for data integration. We use it to feed operational data into a data warehouse. We also use it for creating connections between applications.

    Within our organization, there are a few thousand users of Azure Data Factory.

    We believe that the number of customers and usage of this product will extend over the next few years. For this reason, we invest a lot of resources in building skills, and we make sure to hire consultants who know their way around Data Factory.

    What is most valuable?

    The flexibility that Azure Data Factory offers is great.

    What needs improvement?

    The number of standard adaptors could be extended further. What we find is that if we develop data integration solutions with Data Factory, there's still quite a bit of coding involved, whereas we'd like to move in a direction with less coding and more select-and-click.

    For how long have I used the solution?

    I have been using Azure Data Factory for one year.

    What do I think about the scalability of the solution?

    We don't have any complaints regarding scalability or stability.

    How are customer service and technical support?

    I think Microsofts' technical support does a pretty good job. There is a lot of information available on the internet to find out how to use their products. There's also quite an active community. If you really can't find a solution, you can always call Microsoft. Our organization is partnered with Microsoft, so we usually get answers directly from them.

    Which solution did I use previously and why did I switch?

    We used SSIS. We're still using SSIS. SSIS is an old product. The development of SSIS has more or less stopped and the development is now focused on cloud services — it's the future. Azure Data Factory Is great because it's a cloud service; you do not have to take care of the installation and configuration yourself. The cost buildup is also quite different. I am not sure that's a huge financial advantage yet, but we do believe that it will be in the future.

    How was the initial setup?

    The initial setup was straightforward.

    We didn't have to deploy Azure Data Factory. It's available as an Azure service, so Microsoft takes care of that.

    What's my experience with pricing, setup cost, and licensing?

    It's a pay-per-use model. So if you run it, it's hardly licensing. The entire cost of Azure is per-use. The price you pay is determined by how much you use it.

    What other advice do I have?

    I would definitely recommend Azure Data Factory. On a scale from one to ten, I would give this solution a rating of eight. 

    If there were a larger amount of automated features, I would give them a higher rating. As I mentioned earlier, if we are working on complex applications, then there is a lot of coding involved. What we hope is that over time, there'll be less coding and more "off the shelf" functionality.

    Which deployment model are you using for this solution?

    Public Cloud
    Disclosure: My company has a business relationship with this vendor other than being a customer: Partner
    Buyer's Guide
    Download our free Azure Data Factory Report and get advice and tips from experienced pros sharing their opinions.
    Updated: May 2022
    Buyer's Guide
    Download our free Azure Data Factory Report and get advice and tips from experienced pros sharing their opinions.