Azure Data Factory Reviews

The platform simplifies data access and visualization with minimal coding, catering to various data management needs across different client projects.

The product centralizes data workflows, enhancing data integration efficiency and visualization for comprehensive analysis.

The platform excels in data transformation with its user-friendly interface and robust monitoring capabilities, making ETL processes seamless.

The product integration with advanced coding options could cater to users needing more customization.

I've been using Azure Data Factory for almost a year now.

The product is stable. 

The product is scalable. 

The initial setup process is straightforward. 

Azure products generally offer competitive pricing, suitable for diverse budget considerations.

Data Factory integrates seamlessly within the Azure ecosystem, offering robust data management capabilities across large-scale projects.

I rate it a nine out of ten. 

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. 

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.

When it comes to our business requirements, this solution has worked well for us. However, we have not stretched it to the limit. We use this solution to collect data from multiple data sources and store it in the cloud. We are using it every day and sometimes multiple times a day. 

We use this solution within a limited context, specifically for extracting data and moving it to the Azure Cloud to develop our BI solution. Based on our usage, we have not found any challenges using the solution but have not explored every feature. The one element of the solution that we have used and could be improved is the user interface. 

I have used this solution for four years. 

This is a stable solution. 

We have not had a reason to reach out to the support team. The documentation that they provide has been good enough for our technical team to work out the solution that we needed to use.

I would advise future users of this solution to have a clear definition of their use case. In my experience in certain contexts, PBI worked great for us but we did have concerns around security. The most important thing is to contextualize the use of this tool to work out if it meet the needs of a particular business. 

I would rate this solution a nine out of ten. 

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.

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.

The primary use case of this solution is for data integration.

It has built-in connectors for more than 100 sources and onboarding data from many different sources to the cloud environment.

It has everything we needed.

Being able to post your feedback and queries is a very good feature that is offered by Azure Data Factory.

Understanding the pricing model for Data Factory is quite complex. It needs to be simplified, and easier to understand.

We have experienced some issues with the integration. This is an area that needs improvement.

I have been using this solution for almost two years.

So far it's been stable. We have not experienced any issues.

I haven't put much thought into scalability.

I have contacted technical support.

If there are any queries, they have the provision to post them in the Data Factory GUI and we can contact the team directly. This is a very nice feature.

Any feedback or improvements that we post is acknowledged and taken care of.

The initial setup was straightforward. It was not complex.

Pricing is quite complex.

I have had feedback from many people and many of my team members, and they say that it is difficult to understand. 

Understanding the pricing of Data Factory is quite complex. 

It is not presented in a straightforward way.

I would definitely recommend this solution to anyone who is interested in using it.

I would rate Azure Data Factory an eight out of ten.

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.