Azure Data Factory Reviews

The user interface is very good. It makes me feel very comfortable when I am using the tool.

The solution could use some merge statements.

The solution should offer better integration with Azure machine learning. We should be able to embed the cognitive services from Microsoft, for example as a web API. It should allow us to embed Azure machine learning in a more user-friendly way. 

The solution is scalable.

We've been satisfied with the level of technical support we've received.

The initial setup is very straightforward. The only thing which we had to consider at the beginning was the gateway for the on-premises environment. Otherwise, it's very easy.

We use both the on-premises and cloud deployment models. We typically work with enterprise-level companies.

Azure Data Factory is pretty good but should be considered as an orchestrator, not as an integrated tool. We can use some building components in that tool to orchestrate the entire workflow but if we are thinking about more details, processing, or data modification during the flow, we'd have to consider Azure Databricks or Data Flow for making those calculations or changes. Users will need Azure Data Factory plus third party tools to reach that level of functionally.

I would recommend using Data Factory. I don't have a lot of experience with integration or with integration services, for example, SQL server integration services. However, there are points that should be considered if you are already using SQL server integration services already. You can implement the packages already prepared in Azure Data Factory. It's something that needs to be considered when deciding which technology you are going to use.

I'd rate the solution eight out of ten.

We are working on a data warehouse integration which means that I am working on some big data projects. I'm preparing data for the licensing. One of the projects is preparing data in Azure Data Lake, to run some transformation scripts, perform some ETL processing, and to fulfill the stage layer of the data warehouse. It means that I help with ETL use cases.

I like the feature that allows you to connect to different sources. You can go through forms and click on them and you don't have to provide the scripts. The solution has a good interface and the integration with GitHub is very useful.

There is also a self-service integration that allows you to run the BTS pages on the SQL server from an SIS. Users have an option to configure this self-service integration engine to run items as a part of a pipeline of the Azure data factory.

I think more integration with existing Azure platform services would be extremely beneficial.

In the next release, it's important that some sort of scheduler for running tasks is added.  A built-in scheduling mechanism for running the treasury will be a very helpful improvement.

I've never experienced any issues with the solution's stability.

The scalability of the connected engines makes this solution very scalable.

Although I've had to contact support for other Azure products, I haven't had to for this solution, so I can't speak to any experience with technical support directly.

The initial setup is straightforward. It's got a very nice welcome page where everything explained. Depending on the complexity, deployment takes only a few days. You only need one person for the deployment of the solution. This is dependent, however, on how many pipelines you are implementing. Afterward, you would probably only need one person to maintain it.

I'm an integrator, so I work with clients to help them implement the solution.

I'm not sure of the specific cost, but it is monthly and is somewhere in the ballpark of a few hundred Euros. You don't have to pay for extras. Everything is included in the cost.

I don't use Azure Data Factory for my own company; I help clients implement the solution for their companies.

In terms of advice that I would give to others looking to implement the solution, I would say you have to learn. You have to really understand the overall concept. It does not allow you to just click and go.

I would rate this solution ten out of ten.

Used Azure Data Factory, Data Flow (private preview) and Databricks to develop data integration processes from multiple and varied external software sources to an OLTP application Azure SQL database. The tools are impressively well-integrated, allowing quick development of ETL, big data, data warehousing and machine learning solutions with the flexibility to grow and adapt to changing or enhanced requirements. I can't recommend it highly enough.

This solution will allow the organisation to improve its existing data offerings over time by adding predictive analytics, data sharing via APIs and other enhancements readily. It's as simple as extending the data pipelines with new modules and components. The solution is improving the organisation by offering something the organisation can grow with.

Data Flow and Databricks are going to be extremely valuable services, allowing data solutions to scale as the business grows and new data sources are added.

Data Flow is in the early stages — currently public preview — and it is growing into a tool that will offer everything other ETL tools offer. There are a few features still to come. The thing we missed most was data update, but this is now available as of two weeks ago. A feature that is confirmed as coming soon is the ability to pass in a parameter and filter, etc.

We use Data Factory in our projects, which we deliver to customers. We have almost five implementations in which we're using Data Factory.

It's cloud-based, but there's the integration runtime, which you can connect to on-premises sources. You can transfer data from on-premises to the cloud. We're integrating on-premise data sources to cloud data-based services, like rescue or finance and so on.

Azure Data Factory is very good for enterprise organizations like banks and international insurance companies.

It's cloud-based.

I like the basic features like the data-based pipelines.

We have had some issues, but it's critical to use Data Factory for what it was designed for. It's not very good for iteration processes for loop data activity because you must wait for the runtime. This is a downgrade, but we developed some workaround for it, and we're running the Azure function for these iteration processes.

There's space for improvement in the development process of the data pipelines.

I would rate the stability as nine out of ten, but there are some issues with the responsiveness of the interface.

It's easy to scale.

I would rate the technical support as four out of five.

I'm mostly working with cloud technology, and we use other vendors. On-premises, there is another team in my company that's working with Oracle tools. I'm focusing only on Azure technology, which means that I personally don't have hands-on experience with different tools.

Setup is very straightforward and simple, but it depends on the customers. We have more than 15 data engineers who have data engineer certification from Microsoft. For them, it's a very easy process. 

Of course, there are some issues when configuring the on-premise integration runtime because you have to deal with the network settings on on-premise infrastructure. In terms of ADF as a product, there are very good guides and documentation that you can use to navigate how to solve certain issues.

The price is fair.

I would rate this solution as nine out of ten.

For any customer who needs to transfer or transform the data from sources for analytical purposes, Azure Data Factory is a good product for that.