Azure Data Factory Reviews

I primarily use Data Factory for creating pipelines on cloud in terms of integrating multiple cloud services.

Data Factory's best feature is the ease of setting up pipelines for data and cloud integrations. It also has good integration with other Azure services.

Data Factory could be improved in terms of data transformations by adding more metadata extractions.

I've been using Data Factory for five years.

Data Factory's stability has improved following some initial issues.

Data Factory's scalability is good.

The initial setup was easy as it's a SaaS offering.

Data Factory is affordable.

I would give Data Factory a rating of eight out of ten.

We use Data Factory for automating ETL processes, data management, digital transformation, and scheduled automated processes. My team has about 11 people, and at least five use Data Factory. It's mostly data engineers and analysts. 

Each data analyst and engineer manages a few projects for clients. Typically, it's one person per client, but we might have two or three people managing and building out pipelines for a larger project.  

It's cloud-based, allowing multiple users to easily access the solution from the office or remote locations. I like that we can set up the security protocols for IP addresses, like allow lists. It's a pretty user-friendly product as well. The interface and build environment where you create pipelines are easy to use. It's straightforward to manage the digital transformation pipelines we build.

Data Factory has so many features that it can be a little difficult or confusing to find some settings and configurations. I'm sure there's a way to make it a little easier to navigate.

In the main ADF web portal could, there's a section for monitoring jobs that are currently running so you can see if recent jobs have failed. There's an app for working with Azure in general where you can look at some segs in your account. It would be nice if Azure had an app that lets you access the monitoring layer of Data Factory from your phone or a tablet, so you could do a quick check-in on the status of certain jobs. That could be useful.

We've been using Azure Data Factory for about three years.

I've been happy with it overall. I don't think we've had any major issues. We've been able to do what we needed, whether connecting to different data sources or setting up different types of transformations and processes. 

It's a cloud solution, so it's inherently scalable. I don't know If we have to raise the limits on resources like clusters and processing power or if it will just automatically scale up. I can't remember offhand. 

We managed the same actions with a combination of tools. We used SFTP servers to move data from one place to another. We used scripts for loading and some other stored procedures or processes for data transformation within a database. It took two or three pieces of technology or systems to manage the same types of operation. Data Factory lets us consolidate those steps into a single pipeline. 

Setting up Azure Data Factory is pretty straightforward. We had an Azure account already, and Data Factory was just something we could add as an extra service. We had to create instances and pipelines, and it took us about two weeks to get our first pipelines scheduled and running. 

We do everything in-house.

We see a return on Data Factory if we compare the time and effort that would be necessary to perform the equivalent processes manually. 

I'm not too familiar with the cost, but I believe we're reasonably happy with what we're paying. My understanding is that the cost of Data Factory is tied to consumption. It depends on the amount of data or the number of pipelines running, and the cost varies from month to month depending on the usage. 

You'll obviously pay more if you're scheduling heavy digital transformation processes to run every hour, but I don't think there are any other hidden costs or anything extra. When you set up a new account, you have a trial period that enables you to create a test pipeline or process that's typical of your use case and then do a benchmark test to see if Data Factory can achieve the efficiency you need. You'll also get some idea of how much the process will cost to run. From there, it's straightforward to do a cost evaluation or comparison to see if it's the right fit for your company. 

We were looking for a single solution, and Data Factory was the first one that interested us. I don't think we looked at many others. We were pretty set on Azure, and Data Factory seemed to fit our needs, so we didn't make a full comparison with the alternatives.

I rate Azure Data Factory nine out of 10. It isn't perfect, but it's solid. Data Factory has improved how we deal with various aspects of Azure. It has always met our needs in terms of the transformations and jobs we want to create and schedule. 

Azure Data Factory is an integration tool, an orchestration service tool. It’s for data integration for the cloud.

The solution is okay.

Some stuff can be better, however, overall it's fine.

The performance and stability are touch and go.

The deployment should be easier.

We’d like the management of the solution to run a little more smoothly.

I’ve used the solution for three to five years.

The solution could be more stable. It’s touch and go. It’s not 100%.

For Azure Data Factory, scalability doesn't mean really too much. However, in some scenarios, you can play with it a little bit.

Azure Data Factory is not for users. Is for engineers, for developers. The end user does not interact with Azure Data Factory. There might be 20 developers on the solution currently.

I don't remember a particular scenario right now where I reached out to support. However, when you work with Azure Services, here and there, you might get into some challenges, and maybe sometimes you reach out to Microsoft. That said, I don't remember a particular scenario right now.

It’s hard to describe the installation. It’s not overly complex or extremely easy.

The point is almost true for all services. If you want to do something simple and quick, then it's just a couple of clicks, and it's there. However, in real production environments, it's not like that. You have to arrange a lot of things. You have to set up a lot of things. You have to configure a lot of things correctly in an automated way. That is totally different than just a couple of clicks. You have to put in the work. If you ask me how easy it is, yeah, it is easy. However, it can also be really, really complicated depending on the scenario.

As far as I know, there isn’t any licensing per se for this solution.

I’d rate the solution eight out of ten overall.

I mainly use Data Factory to load data for ETL processes or to Azure Storage and for testing purposes in our business unit.

Data Factory's most valuable feature is Copy Activity.

I've been using Data Factory for around two years.

Data Factory is stable.

We've had no problems with Data Factory's scalability.

Microsoft's technical support is responsive and quick to help.

We used consultants to implement Data Factory.

Data Factory's cost is too high.

I would advise anybody thinking of implementing Data Factory to calculate their costs at the initial stage in order to have some knowledge about future costs for the whole project. I would rate Data Factory as eight out of ten.

We created data ingestion solutions. We have built interpreters, and we have data factories that pull data from our clients. They submit data via Excel spreadsheets, and we process them into a common homogeneous format.

It has helped with some automation. Instead of individual people reviewing these files, we were able to automate the ingestion process, which saved a bunch of time. It saved hours of repeated manual work.

The data mapping and the ability to systematically derive data are nice features. It worked really well for the solution we had. It is visual, and it did the transformation as we wanted.

I couldn't quite grasp it at first because it has a Microsoft footprint on it. Some of the nomenclature around sync and other things is based on how SSRS or SSIS works, which works fine if you know these products. I didn't know them. So, some of the language and some of the settings were obtuse for me to use. It could be a little difficult if you're coming from the Java or AWS platform, but if you are coming from a Microsoft background, it would be very familiar.

For some of the data, there were some issues with data mapping. Some of the error messages were a little bit foggy. There could be more of a quick start guide or some inline examples. The documentation could be better.

There were some latency and performance issues. The processing time took slightly longer than I was hoping for. I wasn't sure if that was a licensing issue or construction of how we did the product. It wasn't super clear to me why and how those occurred. There was think time between steps. I am not sure if they can reduce the latency there. 

I have been using this solution for a year and a half.

It is very stable.

It is very scalable. It is a cloud product. It is being used by business analysts, business managers, and Azure cloud architects. We have just one developer/integrator for deployment and maintenance purposes.

We have plans to increase its usage. We'll be rolling it out for other clients.

Microsoft has these things well-documented. There were videos. I was able to find answers when I needed them. To the uninitiated, it was a little difficult, but we got there.

It was of medium complexity. Because it goes to the cloud, the duration was short. The deployment was minutes and hours.

We are a consultant and integrator. You can use our company for its implementation.

I would rate this solution a nine out of ten.

It's a PaaS service. It's a hybrid solution. The cloud provider is Microsoft.

We are not using Azure Data Factory as for users. Rather, we're using it as a process base. We're just using it for orchestration, not for any kind of ETL stuff.

We have plans to increase usage. It's going to take a major role in any kind of traditional data warehousing. It has big potential, especially as a PaaS offering.

There has been improvement in data resilience, in the way that we're moving the data from on-prem to cloud and vice versa.

The most important feature is that it can help you do the multi-threading concepts. It's in Informatica, but the resourcing is quite robust. You can scale up and scale down as per your needs.

There should be a way that it can do switches, so if at any point in time I want to do some hybrid mode of making any data collections or ingestions, I can just click on a button. I can change a switch and make sure a batch can be a streaming process.

I've been using Azure Data Factory for more than two years.

The stability of Azure as a PaaS could be improved.

It's scalable.

I would rate their technical support 3 out of 5. It's not great, but it isn't bad.

The setup is complex. It has nothing to do with the technology but with the design. We were wondering how to leverage the orchestration layer where we are having the Azure Data Factory and how to integrate with the Databricks. That's where we had some challenges in terms of choosing the right product.

You can do deployment in-house. 

I would rate this solution 8 out of 10.

For someone who is looking to use this solution, my advice is to do proper due diligence of your current application, know where your application is fitting, and look for the requirements. It all depends upon the current use case that you have currently in your system.

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.