Azure Data Factory Reviews

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.

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.

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.

I have been using this solution for the last five years, but probably, the last three years have been significant.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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

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.

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.

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.

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.

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

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.

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

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.

The primary use case is integrating data from different ERP systems and loading it into Azure Synapse for reporting. We use Power BI for the reporting side of it.

We also have customers who are migrating to Azure Data Factory and we are assisting them with making the transition.

It is easy to integrate.

I do not foresee any issues with security.

I find that Azure Data Factory is still maturing, so there are issues. For example, there are many features missing that you can find in other products.

You cannot use a custom data delimiter, which means that you have problems receiving data in certain formats. For example, there are problems dealing with data that is comma-delimited.

I have been using Azure Data Factor for almost one year.

The stability is dependent on how you set up your cloud infrastructure, and how you authorize people to make use of it.

I have not seen any issues with respect to scalability, as it is all hosted within the cloud. We have approximately 20 users.

I have been in contact with technical support, although most of the time I was told that the feature I was interested in was not yet available. In these cases, they will be implementing the missing features in the future.

I use several similar products by different vendors including Talend, Informatica, and Microsoft SSIS. The biggest advantage that Azure has is deployment. However, in others, it is possible to specify custom data delimiters.

The initial setup is pretty simple and it can be deployed in a couple of hours.

I deployed it myself and am also responsible for maintenance.

I would rate this solution a five out of ten.

I use this primarily for ETL tasks.

The most valuable feature is the ease in which you can create an ETL pipeline.

The GUI is very helpful when it comes to creating pipelines. The user interface is also very fast.

The connection to Snowflake is easy. I can store data and transform it during the ETL process before sending it to Snowflake.

Azure Data Factory is a bit complicated compared to Informatica. There are a lot of connectors that are missing and there are a lot of instances where I need to create a server and install Integration Runtime.

The support and the documentation can be improved.

There are a lot of tasks that you need to write code for.

I have been using Azure Data Factory for about six months.

I have experience with Informatica and I find it easier to use. For example, there are a lot of connectors that are directly available. Also, Informatica is able to take incremental copies, but with Azure, you have to write code to do that.

I have also worked with Matillion and Fivetran, and I feel that there are a lot of things that Azure can learn from these products. For example, with Fivetran there are very good connectors for copying data between other solutions. This is unlike Azure, where a lot of the time, I have to build my own logic.

The initial setup is complex.

I would rate this solution a seven out of ten.

I am a manager of a team that uses this solution.

Azure Data Factory is primarily used for data integration, which involves moving data from sources into a data lake house called Delta Lake.

It's fairly simple to use. The most valuable feature of this solution would be ease of use.

It does not appear to be as rich as other ETL tools. It has very limited capabilities. It simply moves data around. It's not very good after that because it's taking the data to the next level and modeling it.

I have been working with Azure Data Factory for less than a year.

I would say that we are working with the latest version.

The stability of Azure Data Factory is good. The performance is good.

I haven't had to scale this solution as of yet.

We have six people in our company who use this solution.

Increasing the usage is not on our strategy pathway.

I have not contacted technical support. I have not required any yet.

I have had very little contact with Microsoft support, but it's been good.

I have also worked with Talend. I didn't switch products, but rather companies.

Talend is a more robust enterprise-wide solution that can handle everything from start to finish, whereas Azure Data Factory is more of an ingestion tool.

I was not involved with the initial setup.

We are an enterprise that uses an integrator.

It does not require any maintenance, it's simple.

I don't see a cost; it appears to be included in general support. I have been told that you have to be very careful because it can blow out. I have not experienced it yet, but I've been warned that as Azure ingestion increases, the costs can rise.

In my opinion, the price is competitive.

It's a good tool, a good product that does what it's supposed to do well, which is ingesting data from a source to your target, to another cloud, to another source. Just be conscious to monitor your costs.

I would rate Azure Data Factory an eight out of ten.

We are currently using it as an ETL (Extract, Transform, and Load) tool. We are using it to connect to various information providers or, in general, to various sources, to extract data, and then to insert it to our storage devices, databases, or data warehouses.

The two most valuable features of Azure Data Factory are that it's very scalable and that it's also highly reliable.

One area for improvement is documentation. At present, there isn't enough documentation on how to use Azure Data Factory in certain conditions. It would be good to have documentation on the various use cases.

Sometimes, it's really difficult to find the answers to very technical questions regarding certain conditions.

I've been using Azure Data Factory since 2019.

It has been stable so far.  

Azure Data Factory is a very scalable solution. Including internal developers and external consultants working for us, we have about 10-15 people using this solution.

We had been using various ETL tools during the years before moving to the cloud. We picked Azure Data Factory because we were moving towards the Azure cloud.

The initial setup is very easy.

We used a consultant as it was a big project. We had five to six specialists, including both internal and external employees, working on it. It took about about three to six months to complete.

Azure Data Factory is a very easy to use tool. If you want to extract, manipulate, and load data to any type of Azure repository, I recommend this solution. However, I would not recommend it if the manipulation of data is very deep and complicated.

I would rate this solution at eight on a scale from one to ten.

When it comes to our business requirements, this solution has worked well for us. However, we have not stretched it to the limit. We use this solution to collect data from multiple data sources and store it in the cloud. We are using it every day and sometimes multiple times a day. 

We use this solution within a limited context, specifically for extracting data and moving it to the Azure Cloud to develop our BI solution. Based on our usage, we have not found any challenges using the solution but have not explored every feature. The one element of the solution that we have used and could be improved is the user interface. 

I have used this solution for four years. 

This is a stable solution. 

We have not had a reason to reach out to the support team. The documentation that they provide has been good enough for our technical team to work out the solution that we needed to use.

I would advise future users of this solution to have a clear definition of their use case. In my experience in certain contexts, PBI worked great for us but we did have concerns around security. The most important thing is to contextualize the use of this tool to work out if it meet the needs of a particular business. 

I would rate this solution a nine out of ten. 

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. 

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. 

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.

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.

I've been using this solution for five years. 

From what I've seen this is a stable 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.

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. 

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.

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.

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.

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.