Azure Data Factory Reviews

The features that I've found most valuable, in order: That it is a complete ETL Solution, the second one is interface, the third one workflow, and the fourth one ease of use.

The only thing that we're struggling with is increasing the competency of my team. So we think that the Microsoft documentation is too complicated.

I would like to see it more connected. I know they're working on the Snowflake data warehouse connector, but more connectors would be helpful.

I've been using Azure Data Factory for a few months.

It is stable.

Azure Data Factory is a scalable solution.

In terms of support, we haven't used the direct support but the community support is very good.

We previously used the Oracle Warehouse Builder. The reasons we stopped using it were that Warehouse Builder is at the end of its life-cycle and its slow performance.

The initial setup is complex.

We first implemented it ourselves but we're using a partner now. We thought we could do it ourselves but we needed specialty help.

I would highly recommend Azure Data Factory as it has proof of concept.

We have nine people using Azure Data Factory.

On a scale of one to ten, I give it an eight.

We use this solution for data integration. We use it to feed operational data into a data warehouse. We also use it for creating connections between applications.

Within our organization, there are a few thousand users of Azure Data Factory.

We believe that the number of customers and usage of this product will extend over the next few years. For this reason, we invest a lot of resources in building skills, and we make sure to hire consultants who know their way around Data Factory.

The flexibility that Azure Data Factory offers is great.

The number of standard adaptors could be extended further. What we find is that if we develop data integration solutions with Data Factory, there's still quite a bit of coding involved, whereas we'd like to move in a direction with less coding and more select-and-click.

I have been using Azure Data Factory for one year.

We don't have any complaints regarding scalability or stability.

I think Microsofts' technical support does a pretty good job. There is a lot of information available on the internet to find out how to use their products. There's also quite an active community. If you really can't find a solution, you can always call Microsoft. Our organization is partnered with Microsoft, so we usually get answers directly from them.

We used SSIS. We're still using SSIS. SSIS is an old product. The development of SSIS has more or less stopped and the development is now focused on cloud services — it's the future. Azure Data Factory Is great because it's a cloud service; you do not have to take care of the installation and configuration yourself. The cost buildup is also quite different. I am not sure that's a huge financial advantage yet, but we do believe that it will be in the future.

The initial setup was straightforward.

We didn't have to deploy Azure Data Factory. It's available as an Azure service, so Microsoft takes care of that.

It's a pay-per-use model. So if you run it, it's hardly licensing. The entire cost of Azure is per-use. The price you pay is determined by how much you use it.

I would definitely recommend Azure Data Factory. On a scale from one to ten, I would give this solution a rating of eight. 

If there were a larger amount of automated features, I would give them a higher rating. As I mentioned earlier, if we are working on complex applications, then there is a lot of coding involved. What we hope is that over time, there'll be less coding and more "off the shelf" functionality.

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.

In my new organization, we are currently analyzing this solution. In my previous organization, I have worked with Azure Data Factory and Databricks.

It is deployed in a virtual private cloud.

Its integrability with the rest of the activities on Azure is most valuable.

It is also easier to work with. It has a nice user interface that would seem very familiar to some of the people who are coming from Microsoft SSIS and other similar kinds of activities. It makes it easy for them to pick it up quickly.

It can improve from the perspective of active logging. It can provide active logging information.

It should provide support for changing data capture on several other platforms.

I have been using this solution for two years.

It is scalable. From the security perspective, it depends on how we implement it. It depends on the organization, but I don't see a challenge. 

In my previous organization, we had close to a hundred people.

Their technical support is very good.

It was very easy.

I would generally recommend this solution if you have an Azure setup or Azure as a baseline.

I would rate Azure Data Factory an eight out of 10.

The support for SAP services and databases, specifically SAP HANA, has been a game-changer for us.

It would be helpful if they could adjust the data capture feature so that when there are source-side changes ADF could automatically figure it out.  

The solution needs to integrate more with other providers and should have a closer integration with Oracle BI.

I've been working with the solution for just over a year.

From what we have seen so far, the solution seems very stable.

The solution is scalable. Right now, we have only three or four people on it. We may increase usage int eh future.

We have direct technical support in Israel. In India, we haven't used it yet. However, for other services, the help provided is fine. 

The initial setup was pretty fast and simple. I wouldn't say it was too complex. For deployment, it depends on your environment. In our POC, it didn't take more than two to three hours. In the production environment, however, it takes a bit more time for operations.

In general, we've been satisfied with the pricing. However, since it's a service based on usage, it's really hard to estimate the cost in advance for this product. This makes it hard to quote. It's not so easy to estimate the costs in advance when giving a quote. That makes it difficult to convince some customers because most would like to see a number in advance. 

We primarily use the solution in a hybrid environment. We're Azure partners.

I would suggest others start with a small POC and with a smaller dataset or simple pipeline to test the product before fully implementing it. Users shouldn't hesitate to try it, because there are not so many risks. Practice is the best way to start with this service, as opposed to planning. 

I'd rate the solution eight out of ten.