Azure Data Factory Reviews

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. 

Our company uses the solution for data ingestion. 

It is beneficial that the solution is written with Spark as the back end. 

The solution is cloud-based and integrates well with other Azure products such as Synapse Analytics.  

There are limitations when processing more than one GD file. 

Data ingestion pipelines sometimes fail because of transient issues that have to do with the cloud network. It takes more than six hours to process or ingest 300,000 records and that is a long time. 

I have been using the solution for two years. 

The solution is new in the market and pretty stable because ADF is a little more codified than AWS. Synapse Analytics adds another tool for data. 

Stability is not quite at the level of Informatica or DataStage. 

The solution is scalable. 

For multi-tenant applications connected to multiple databases, Microsoft recommends a share box and a cell post integration run time. But a run time connecting to multiple sources has limitations and requires multiple shares connecting to your data if you are ingesting it from on-premises. 

Technical support is okay. Support is contracted or partnered with various companies but is fine as a first level. 

Most of the time, technical support has to connect with product engineers who troubleshoot issues. 

The setup is not very complex but requires intake, setting up integration services, and connecting to databases like Oracle before you push it to service.

We implemented the solution in-house. 

The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper. 

In the cloud, everything is service based and expensive. Users should be knowledgeable enough to maximize the solution. 

For example, it makes no sense to run integration services all day if you are not ingesting data because you pay for that usage. It is important to understand how the product works to manage it accordingly and keep costs down. 

I rate the solution a six out of ten. 

I primarily use Data Factory for data ingestion and B2B transformation.

Data Factory's best features are connectivity with different tools and focusing data ingestion using pipeline copy data.

Data Factory's monitorability could be better. In the next release, Data Factory should include integrations with open-source tools like Air Flow.

I've been working with Data Factory for about a year.

Data Factory is stable.

Data Factory is scalable.

Microsoft's technical support is good, so long as your company has a good relationship with them.

I previously worked with Talend, Matillion, and Fivetran.

Data Factory is expensive.

I would rate Data Factory seven out of ten.

We mainly use this solution to carry out data movement and transformation.

This solution has provided us with an easier, and more efficient way to carry out data migration tasks.

This solution is currently only useful for basic data movement and file extractions, which we would like to see developed to handle more complex data transformations.

We have been using this solution for a year.

We have found this to be a stable solution.

This is an easily scalable product, due to it being cloud-based.

The customer support for this solution is very good. 

The initial setup of this product is straightforward, if you deploy the solution using a template; rather than implementing the solution first, and configuring the features afterwards.

We implemented the product using both in-house staff, and members of a vendor team. The vendor team were very helpful, and gave good advice while we were deploying the solution.

We would recommend this solution as it is very solid and has good security features.

I would rate this solution an eight out of 10.

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.

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.

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.