Azure Data Factory Reviews

We use this solution to quickly instantiate certain components in Azure with the aim of having consistent use of certain components and objects. It provides one point of reference rather than having the need to replicate points of references, and then having to keep them in sync. 

It has helped our company by providing consistency. For example, by making sure that the way certain objects and components are defined is consistent throughout every step. If things change at any point, they should be reflected at all points. It's the consistency that makes implementations less buggy and less complex.

The function is the central point of reference and the most valuable thing about Data Factory.

I'd like to see videos on YouTube or the Microsoft site with more detailed implementations. The solution lacks a decent UI that allows us to see what kinds of requests are for what objects and how the population of objects is being requested and compared. Right now we have to look at logs to get an idea of what types of calls the data factory receives in what sequence, for example. It would be nice to be able to see it graphically because we currently have to interpret the logs and then create a graphical representation to have an idea of what's going on. In general, it could be simplified and made more user-friendly.

I've been using this solution for six months.

This solution is stable. 

There are thousands of users so the solution is scalable. 

The customer service is excellent. 

Positive

I previously used a combination of solutions to achieve the same end. Data Factory simplifies things which is why we switched to it. 

The initial setup was complex. The deployment was carried out in-house and we had around 10 people involved in the implementation. 

In terms of time and effort savings, we have a return on our investment. 

It's important to have a good data model before you start using the solution; an idea of the types of data architecture, data objects and components in order to use them. Because of the lack of more user-friendly interfaces, especially for the people debugging the system, I rate this solution eight out of 10.

We primarily use the solution in a data engineering context for bringing data from source to sink.

The solution is very comfortable to use. I'm happy with the user interface and dashboards. I'm pretty happy with everything about the solution. 

We haven't had any issues connecting it to other products.

It's a stable product. 

I have not found any real shortcomings within the product.

I've been using the solution for the past year. 

The product has been very stable and reliable. I'd rate the stability nine out of ten. There are no bugs or glitches. It doesn't crash or freeze. 

There is a team of 30 people working on the solution. 

I've connected with technical support a few times. 

They sent a support engineer or a field engineer to us, and he helped us out. 

Positive

I'm not sure about the exact cost of the solution. 

I'm a customer and end-user.

Our company chose to use this solution based on the fact that it is a Microsoft product. We're using a lot of solutions, including Outlook and Teams. We also use Power BI. We try to use Microsoft so that everything is under one umbrella. That way, there is no difficulty with connecting anything. 

It's a good solution to use. There are lots of videos available on YouTube, and it is very easy to learn. It's very easy to perform things on it as well, which is one thing that a product like ThoughtSpot lacks. There is no training needed like Power BI. 

I'd rate the solution nine out of ten.

My clients use Data Factory to exchange information between the on-premises environment and the cloud. Data Factory moves the data, and we use other solutions like Databricks to transform and clean up the data. My teams typically consist of three or four data engineers.

I like how you can create your own pipeline in your space and reuse those creations. You can collaborate with other people who want to use your code.

DataStage is easier to learn than Data Factory because it's more visual. Data Factory has some drag-and-drop options, but it's not as intuitive as DataStage. It would be better if they added more drag-and-drop features. You can start using DataStage without knowing the code. You don't need to learn how the code works before using the solution.

I think the communication about the ADA's would be interesting to see in the platform. How to interact with those kind of information and use it on your pipelines.

I have used Data Factory for eight months.

I have never experienced downtime with Data Factory. 

It isn't that expensive to scale Data Factory up. My client can ask for more resources on the tool, and paying more is never an issue. 

I rate Azure support seven or eight out of 10. There is room for improvement. Sometimes, you don't know where the errors originate. You have to send a ticket to Azure, and they take two or three days to respond. The issue may resolve itself by then. The problem is fixed, but you don't know how to prevent it or what to do if it happens in the future. 

The data transfer has stopped a few times for unknown reasons. We don't know if the resources are insufficient or if there is a problem with the platform. By the time we hear back from Microsoft, the issue has been resolved.

Positive

Data Factory is effortless to set up.

I rate Azure Data Factory nine out of 10. When implementing Data Factory, you should document where you are building so you can pass that information. Sometimes you build something for a specific purpose, but you can use that information for other solutions. If you have a community where you are building things, you can reuse them on the platform, so don't need to build everything from scratch.

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.

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.

We use Azure Data Factory for data transformation, normalization, bulk uploads, data stores, and other ETL-related tasks.

Azure Data Factory allows us to create data analytic stores in a secure manner, run machine learning on our data, and easily adapt to changing schema.

The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring.

The documentation could be improved. They require more detailed error reporting, data normalization tools, easier connectivity to other services, more data services, and greater compatibility with other commonly used schemas.

I would like to see a better understanding of other common schemas, as well as a simplification of some of the more complex data normalization and standardization issues.

It would be helpful to have visibility, or better debugging, and see parts of the process as they cycle through, to get a better sense of what is and isn't working.

It's essentially just a black box. There is some monitoring that can be done, but when something goes wrong, even simple fixes are difficult to troubleshoot.

I have been working with Azure Data Factory for a couple of years.

There is only one version.

Overall, I believe the stability has been good, but there have been a couple of occasions when Microsoft's resources needed to be allocated were overburdened, and we had to wait for unacceptable amounts of time to get our slot. It has now happened twice which is not ideal.

There is no limit to scalability.

We only have a few users. One is a data scientist, and the other is a data analyst.

We use it to push up various dashboards and reports, it's a transitional product for transferring, transforming, and transitioning data.

It is extensively used, and we intend to expand our use.

You don't really get that kind of support; it's more about documentation and the community support that is available. I would rate it a three out of five compared to others.

You could call them, and pay for their consulting hours directly, but for the most part, we try to figure it out or look through documentation. 

I think their documentation is lagging because it's not as popular of a tool, there's just not a lot, or as much to fall back on.

Neutral

We had only our own tools, and we switched because you get to leverage all of the work done in a SaaS or platform as a service, or however they classify it. As a result, you get more functionality, faster, for less money.

The initial setup is straightforward.

It is a working tool. You can start using it within an hour and then make changes as needed.

We only need one person to maintain the solution; it doesn't take much to keep it running.

It's not a problem; it's a platform.

We completed the deployment ourselves.

We have seen a return on investment. I can't really share many details, but for us, this becomes something that we sell back to our clients.

You pay based on your workload. Depending on how much data you process through it, the cost could range from a few hundred dollars to tens of thousands of dollars.

Pricing is comparable, it's somewhere in the middle.

There are no additional fees to the standard licensing fee.

We looked at some other tools, such as Databricks, AmazonGlue, and MuleSoft.

We already had most of our infrastructure connected to Azure in some way. So the integration of where our data resided appeared to be simpler and safer.

I believe it would be beneficial if they could find someone experienced in some of the tools that are a part of this, such as Spark, not necessarily Data Factory specifically, but some of those other tools that will be very familiar and have a very quick time for productivity. If you're used to doing things in a different way, it may take some time because there isn't as much documentation and community support as there is for some more popular tools.

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

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

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. 

I have been using this solution for the past year. 

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.

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. 

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. 

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. 

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. 

I implemented through an in-house team. 

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.

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.

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.