Azure Data Factory Reviews

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.

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.

Depending on their pipeline, our customers use Azure Data Factory for their ELT or ETL transformation processes.

I like that it's a monolithic data platform. This is why we propose these solutions.

The performance could be better. It would be better if Azure Data Factory could handle a higher load. I have heard that it can get overloaded, and it can't handle it.

We have been providing customers Azure Data Factory solutions for about five years. 

Azure Data Factory is a stable solution. The performance is good.

Microsoft technical support is good. We are a Gold partner, and we have got good tech support from them.

From a cloud perspective, the initial setup is straightforward. We have a data engineering team with about 15 professionals managing and maintaining this solution.

We have an accelerated solution around it. We utilize our accelerated solutions to spin all these services into the cloud. So, for us, it does not take much time.

I would recommend this solution depending on whether they want AWS, Azure, or GCP. We recommend all of them to our customers. We have about 50 to 80 people who are using this solution.

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

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.

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.

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.

Azure Data Factory is a stable 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.

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.

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.

The initial setup is easy.

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

Pricing appears to be reasonable in my opinion.

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.

We use Azure Data Factory to connect to clients' on-premise networks and data sources to bring the data into Azure. Additionally, Azure Data Factory orchestrates data movement and transformations. It can connect to a number of different cloud data sources to bring the information into something, such as a data lake or a formal SQL database. Azure Data Factory has the ability to handle large data workloads and can orchestrate them well.

The most valuable features of Azure Data Factory are the flexibility, ability to move data at scale, and the integrations with different Azure components.

Azure Data Factory can improve by having support in the drivers for change data capture.

I have been using Azure Data Factory for approximately three years.

Azure Data Factory is a very reliable and stable solution.

The solution is highly scalable.

The technical support is very good, they are responsive.

We previously use Attunity and we switch to Azure Data Factory mainly because of cost reasons and integration.

The biggest difference between Azure Data Factory and Attunity is Attunitys has the ability to perform change data capture. Whereas Azure Data Factory is more centered around batch or bulk loads.

The initial setup is of a moderate level of difficulty. However, it can be complex. The solution is able to fit both of our use cases.

We normally use one or two people to update and maintain Azure Data Factory.

There's no licensing for Azure Data Factory, they have a consumption payment model. How often you are running the service and how long that service takes to run. The price can be approximately $500 to $1,000 per month but depends on the scaling.

My advice to others that want to implement Azure Data Factory is to use a metadata approach.

I rate Azure Data Factory an eight out of ten.

We use this solution to perform ELTs so that we do not need to keep code within a database.

The overall performance is quite good.

Occasionally, there are problems within Microsoft itself that impact the Data Factory and cause it to fail.

I've worked with this solution for two and a half years.

I wouldn't consider it to be stable since it fails at times.

The solution is scalable.

Support is quite slow and they have bugs that they are unaware of and claim that that is how the system is supposed to work.

My company used Informatica PowerCenter in the past but I was not involved in that.

The initial setup was quick and easy. The whole process took about fifteen minutes. We have about a hundred users at the moment and have plans to increase.

Two of our in-house developers were able to complete the setup.

This solution has good performance but could use better stability. I would rate this a nine out of ten.

My primary use cases for this solution are integration and connecting to the different data stores where we get data and migration activity, deployment, and integrations into using linked services and deployment models.

This solution has allowed me to quickly get analysis, sales data, supply chain data, and eCommerce data.

The most valuable feature of this solution is that it allows more data between on-premises and cloud solutions. It's also useful for orchestration for complex data flows and allows us to do ETL-based transitions heavily. In addition, it allows us to integrate with other third-party systems and compare features and pricing. Other valuable features include database replication, SQL service products, SLA support, data sharing, vendor lock-in, and support for developer tools.

Areas for improvement would be the product's performance and its mapping of data flow. In addition, some of the optimization techniques are not scalable, some naming connections are not supported, and automated testing is not supported in all cases. In the next release, I would like to see support so we can enhance based on the next-level pipelines, writing from scratch, flexible scheduling, and pipeline activity.

I've been using this solution for about a year.

This solution is very reliable.

This solution is scalable.

I am satisfied with the technical support.

I previously worked with Azure SQL database.

The initial setup was complex, but the deployment only took 30 to 40 minutes.

This product is priced at the market standard, which is good given that the product contains all the available assets.

When selecting services, make sure to choose only those you need in order to reduce your costs. I would rate this solution as 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.