Azure Data Factory Reviews

I like Azure Data Factory, it works quite well.

It is always effective. It's extremely consistent.

The only thing I wish it had was real-time replication when replicating data over, rather than just allowing you to drop all the data and replace it. It would be beneficial if you could replicate it.

Real-time replication is required, and this is not a simple task.

We are using the latest version of Azure Data Factory.

I have no issues with the stability of the Azure Data Factory. We have not had any glitches.

Azure Data Factory is extremely simple to scale.

This solution is used by a dozen people in our company.

I have not contacted technical support. We haven't needed the assistance of technical support.

We use Microsoft products such as Azure Databricks.

Azure Data Factory is the only solution that I have used since I started with this company.

The initial setup is pretty straightforward. However, there is a bit of a learning curve, but once you get it, it's pretty simple.

It's not particularly expensive.

It works very well.

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

We are a tech services company and this is one of the tools that we use when implementing solutions for our clients. I am currently managing a team that is working with the Azure Data Factory.

Our clients that use this solution are migrating their data from on-premises to the cloud.

One of our clients is building an integrated data warehouse for all of their data, using this solution. It is used to extract all of the data from different servers and store it into one place.

Our clients find that this solution has a very good performance. They like the speed.

It is easy to deploy workflows and schedule jobs. You can just click on the desktop and it works.

The setup and configuration process could be simplified.

We have been using Azure Data Factory for the past six months.

This is a stable product and we're expecting more updates from Microsoft. We have not used more than one terabyte of data so that remains untested, but for one terabyte it works fine.

Development is only done on an occasional basis, but the solution is used every day. If it is streaming data then the process is continuous, otherwise, it is initiated by the user on demand.

This solution is 100% scalable.

We have two clients working with this solution.

It is another team who is responsible for contacting technical support.

We have used other ETL solutions in the past, and Azure Data Factory is the best one. Compared to SSIS, for example, ADF is easier to use and the performance is better.

Our clients are migrating from on-premises SSIS solutions to the cloud because they want to take advantage of the latest technologies.

The installation is very simple and it doesn't take much time. For us, the deployment took about two days, which does not seem unreasonable for something that is on the cloud. Most of the time is spent waiting for credentials.

Depending on the sources of the data, four people are required for deployment and maintenance. If the sources are SQL databases then it is straightforward and four people can cope with it. If the data is more difficult then we may need more people.

The deployment is done in-house with a technical person with knowledge of the Azure cloud.

This is a cost-effective solution.

We have another team that is moving to AWS, but for now, we will continue working with Azure Data Factory. Once they have explored the AWS solution fully, we will compare the two.

Within the next six months, we are planning to enter into the machine learning part of this solution. This is a product that I can recommend.

I would rate this solution an eight out of ten.

We primarily used this solution for getting data from a client's server, or online data, to an Azure Data Lake. We create pipelines to orchestrate the data flow from source to target.

The most valuable feature is the copy activity. Instead of creating a pipeline and then doing a source to target copy, this feature will create the pipeline directly.

Snowflake connectivity was recently added and if the vendor provided some videos on how to create data then that would be helpful. I think that everything is there, but we need more tutorials.

We have been working with Azure Data Factory for the past two years.

Occasionally, there are timeout issues that happen when connecting to the source or the target. Normally, you just hit refresh and it will connect properly.

Being a cloud product, it can scale as much as we need.

In my project, there are ten people using this product.

I have been in contact with technical support and it's good, as long as you directly connect with the appropriate technical person right away. When you raise an issue, sometimes the people who are available are unfamiliar with that particular technology, so they have to route the issue to the concerned person. This routing takes at least one or two days, so if you can directly get the corresponding person allocated to the ticket, that would be great. 

The initial setup is simple and it only takes perhaps five minutes to deploy.

When one of our developers wants to use it, they can create their own jobs.

Our deployment was completed by an in-house team.

No maintenance is required and when we experience problems, we normally solve them on our own.

The licensing is a pay-as-you-go model, where you pay for what you consume.

If you have Azure as a cloud service and you want to perform ETL then Azure Data Factory is a product that I can recommend.

I would rate this solution a six out of ten.

We use the solution to gather data from four separate operational systems.

The data copy template is a valuable feature. With the pipeline template, it takes only a few clicks for the on-premises data to come in. 

There is no built-in function for automatically adding notifications concerning the progress or outline of a pipeline run. I believe this requires a bit of development through power ups, but it would be nice to have a task for firing off an email through the GUI.

I have been using Azure Data Factory for around six months.

When it comes to stability, the solution is similar to the previous Azure Synapse Analytics. They are both very easy.

When it comes to scalability, the solution is similar to the previous Azure Synapse Analytics. They are both very easy.

The licensing cost is included in the Synapse.

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

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 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.

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.

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.