Azure Data Factory Reviews

Data Factory's best features include its data source connections, GUI for building data pipelines, and target loading within Azure. It's also highly configurable and integrates well with the rest of the Azure services.

Data Factory's performance during heavy data processing isn't great.

Data Factory is stable - I have customers running thousands of jobs a day without problems.

Data Factory is scalable.

Microsoft's technical support is pretty good.

The initial setup is complex because there are a lot of prerequisites, including plumbing in the network, but that's typical for any cloud-based solution.

Data Factory is a good, mature solution, and I would rate it as eight out of ten.

We had an old, traditional data warehouse. We decided to put it into the cloud and we used Azure Data Factory to reform the EEL process from SQL server integration services to extra data.

Azure Data Factory was chosen by the team that I was not on at the time and who decided that this would be the move to the future. So I just went along with it.

In terms of my personal experience, it works fine.

We didn't have a very good experience. The first steps were very easy but it turned out that we used Europe for a Microsoft data center, also partly abroad for our alpha notes. As soon as we started using Azure Data Factory, the bills got higher and higher. At first we couldn't understand why, but it is very expensive to put data into a data center abroad. So instead, we decided to use only Northern Europe, which worked out for a while in the beginning. And then we had nothing to show for it. They gave me a really hard time for this.

Azure Data Factory should be cheaper to move data to a data center abroad for calamities in case of disasters.

What I really miss is the integration of Microsoft TED quality services and Microsoft Data services. If they were to combine those features in Data Factory, I think they would have a very strong proposition. They promise something like that on Microsoft Congress. That was years ago and it's still not here.

I have been using Azure Data Factory for a couple of years now, since about 2017.

Azure Data Factory is a stable solution.

Yes, Azure Data Factory is scalable.

We previously used SSIS because of Microsoft.

The installation is straightforward.

We use data engineers to do the install.

We pay monthly for this.

On a scale of one to ten, I would give Azure Data Factory a seven. Compared to Informatica, it's really crude. I think it's a very crude solution. 

Would I recommend Azure Data Factory? It depends if they need a straight reading in data, then I would say it's perfect. But with Informatica, you can do data storing and data quality checks - there is a lot there than just a data center.

I think Azure Data Factory is a mature product. We used Version One in my project and a lot of it isn't possible on this version. The Version Two is much faster and much better. It's not at the same level as Informatica.

I use Azure Data Factory in my company because we are implementing a lot of different projects for a big company based in the USA. We're getting certain information from different sources—for example, some files in the Azure Blob Storage. We're migrating that information to other databases. We are validating and transforming the data. After that, we put that data in some databases in Azure Synapse and SQL databases.

I think it makes it very easy to understand what data flow is and so on. You can leverage the user interface to do the different data flows, and it's great. I like it a lot.

It would be better if it had machine learning capabilities. For example, at the moment, we're working with Databricks and Azure Data Factory. But Databricks is very complex to do the different data flows. It could be great to have more functionalities to do that in Azure Data Factory.

I have been using Azure Data Factory for about one year.

Azure Data Factory is a stable solution.

It's scalable. We're doing a lot of different integrations with a lot of data, and scalability is great.

The initial setup is straightforward. I think that it's so easy to start a project using that technology.

We have a team that's in charge of doing the deployments in Azure in different environments.

I would tell potential users that there are many technologies to do this. For example, if you like to manage big data and do something with it, it would be better to use Databricks.

On a scale from one to ten, I would give Azure Data Factory a nine.

The solution is primarily used for data integration. We are using it for the data pipelines to get data out of the legacy systems and provide it to the Azure SQL Database. We are using the SQL data source providers mainly.

The data pipeline and the orchestration functionality are the most valuable aspects of the solution.

The interface is very good. It seeks to be very responsive and intuitive.

The initial setup is very quick and easy.

I'm more of a general manager. I don't have any insights in terms of missing features or items of that nature.

Integration of data lineage would be a nice feature in terms of DevOps integration. It would make implementation for a company much easier. I'm not sure if that's already available or not. However, that would be a great feature to add if it isn't already there.

We've used the solution for the last 12 months or so.

From what I have witnessed, the solution is quite stable. It doesn't crash or freeze. There are no bugs or glitches. It's reliable.

We work with medium to enterprise-level organizations. Customers have anywhere from 300 employees up to 160,000 employees.

Microsoft offers a great community. There's a lot of support available. We're quite satisfied with the level of assistance on offer.

Since the solution is a service, it's basically just a click and run setup. It's very simple. There's very little implementation necessary. A company should be able to easily arrange it. The deployment doesn't take very long at all.

We do provide the implementation for our clients. We're able to provide templates as well. We have predefined implementation space in Data Factory and provide it to the customer.

While clients might individually evaluate other options, however, we're not aware of that information. I can't say what other solution clients might consider before ultimately choosing Microsoft. I would say that it is likely Talend and maybe SQL Server Integration Services.

We are like an integrator. We are a data warehouse NPI consulting company and we use Data Factory to pull data from different legacy systems and do all these transformations that are necessary in order to provide analytical models.

In our normal scenario is that we are providing Azure SQL Databases together with Azure Data Factory and Power BI. 80% of our customers have recognized such a scenario.

On a scale from one to ten, I'd rate the solution at an eight. We've been largely happy with the capabilities of the product.

There was a need to bring a lot of CRM and marketing data for some PNL analysis. We are connecting to the Salesforce cloud. In it, there's a specific solution in Salesforce Core CRM for the pharmaceutical industry. We are using the solution to connect to that and we are bringing in the various dimensions and transactions from that data source.

Data Factory itself is great. It's pretty straightforward. You can easily add sources, join and lookup information, etc. The ease of use is pretty good. 

They have a lot of other components like a newer monitor, which helps track and generate alerts for any failed jobs and things of that nature, which is helpful.

At this point in time, they should work on somehow integrating the big data capabilities within it. I have not explored it, but it would be good if somehow we could call a Spark job or something to do with the Spark SQL within ADS so that we wouldn't need a Spark tested outside.

On the UI side, they could make it a little more intuitive in terms of how to add the radius components. Somebody who has been working with tools like Informatica or DataStage gets very used to how the UI looks and feels. 

In ADS, adding a new table or joining a new table and overriding that with an override SQL that I could customize would be helpful.

Being able to debug from the design mode itself would be helpful.

I've been using the solution for one year.

In the latest version, the v2 version, the solution is pretty stable. It does not give unknown letters or things like that.

The solution allows you to create reusable components, so it can be scaled pretty easily.

Being an IT services company, we have a gold or a platinum partnership with Microsoft. For us, getting the technical support we need is not a big issue. Their community is also pretty active in responding to any issues. It's quite good. We've been satisfied with the level of support that is offered.

We were not actually involved in the initial setup. That was all with the client, so I won't be able to comment on it.

In terms of licensing costs, we pay somewhere around S14,000 USD per month. There are some additional costs. For example, we would have to subscribe to some additional computing and for elasticity, but they are minimal. 

For disaster recovery and readability setups, we did that on Data Lake.

We use the public cloud deployment model.

I'd warn others to ensure that the design should be frozen before you start building because overriding each other's code and managing code takes effort. To avoid or to reduce that effort, ensure that the design is frozen. You can build some configurable code rather than hard-coding everything into the jobs. That's the biggest recommendation.

I'd rate the solution seven out of ten. It's a pretty good solution, but over the past year, I've been limited on the number of cases I have on it. If it had a better user interface and was more intuitive I would have rated it higher.

Our primary use case is for traditional ETL; moving data from the web and data sources to a data warehouse. I've also used Data Factory for batch processing and recently for streaming data sets. We have a partnership with Microsoft and I am a cloud solution architect.

There are a lot of innovative features that Microsoft releases regularly. The UI is easy to navigate and finding information on new features has proven to be quite easy. I like that I can retrieve VTL code pretty quickly without knowing in-depth coding languages like Python. Microsoft has very good support teams that I've dealt with and they are very helpful in resolving problems.

Improvement could be made around streaming data because I feel that the Data Factory product is mainly geared for batch processing and doesn't yet have in-built streaming data processing. Perhaps it's on the way and they are making some changes to help facilitate that. If they were to include better monitoring that would be useful. I'd like to see improved notifications of what the actual errors are.

I've been using this solution for four years. 

The solution is stable. 

The way we've used the product is around streaming data, and that doesn't work well with Data Factory. As loads increase, some of the underlying infrastructure that gets used to process data seems to slow down. This is basically a development product, so in terms of scalability it doesn't have a wide user base, it's only meant for developers and analysts. The number of users will vary from anywhere between one to five people. 

The customer service is excellent. 

I've previously used Microsoft SSIS, which is the last incarnation of Azure Data Factory. I've also used IBM Data Manager and Teradata's Management Loads, so I have quite a bit of experience in this area. One of the major differences is that Azure Data Factory is a SaaS service. The other solutions were in-house and you managed your own infrastructure to run them. What I get from Data Factory is a lot better than what I got for the other products. Azure Data Factory is great because it's evolving day-to-day.

The initial setup is relatively straightforward. That's assuming that when you're creating the Data Factory, you have some knowledge about how to create it. We deployed in-house and it took about ten minutes for the initial creation process. The provisioning itself is not something you have control over because it's a self-service that does what it needs and happens in the background. The infrastructure doesn't require any maintenance, it's all managed on the backend. There is maintenance in terms of your codes, connections, and the like, but that is separate from the infrastructure maintenance.

The Data Factory itself will not give you a return on investment, it's the entire solution that brings a return, I'd say at least 20% to 30% of return for investment over a five-year period. 

Azure Data factory is a pay-as-you-go service so cost depends on the number of connections, how many times each activity in that node is run, and how much data gets moved. There are a number of factors that define the price, but it pairs with your service. You're charged for the amount of data that's moved, but there are no charges for the features you use.

I would definitely recommend this solution. If you decide to implement Data Factory, I suggest reaching out to qualified professionals because there are a lot of moving parts. That said, if you have internally qualified staff, deployment shouldn't be a problem. Apart from a few minor issues, it's pretty reliable with good support and a whole bunch of resources available on the web.

I rate this solution a solid nine out of 10. 

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.