Azure Data Factory Reviews

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.

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 for data transformation and data loading. It works from your transaction systems, and we are using it for our HRMS, Human Resource Capital Management System. It picks up all the transactional data pick and moves into the Azure Data Warehouse. From there, we would like to create reports in terms of our financial positions and our resource utilization project. These are the reports that we need to build onto the warehouse.

The purpose of Azure Data Factory is more about transformations, so it doesn't need to have a good dashboard. But, it has a feeding user interface for us to do our activities and debug actions. I think that's good enough.

Azure Data Factory's most valuable features are the packages and the data transformation that it allows us to do, which is more drag and drop, or a visual interface. So, that eases the entire process.

Azure Data Factory setup is quite user-friendly.

I am happy with the interface.

We are too early into the entire cycle for us to really comment on what problems we face. We're mostly using it for transformations, like ETL tasks. I think we are comfortable with the facts or the facts setting. But for other parts, it is too early to comment on.

We are still in the development phase, testing it on a very small set of data, maybe then the neatest four or bigger set of data. Then, you might get some pain points once we put it in place and run it. That's when it will be more effective for me to answer that.

We are building Azure Data Factory right now internally to extract data from our transactional systems and put them into the warehouse so that the reporting engine can be built too.

We have not tried it scaling up. But, Azure promises the stability and scalability should not be an issue.

From a development perspective, I think there were four developers who use Azure Data Factory. From a warehouse perspective, once we roll out the reports out, it should be used by at least 40 or 50 people minimum.

Generally, the documentation is pretty decent. All the issues that come up are here in the documentation part. We've not really had to go to Microsoft as of now from a support perspective. The documentation and the support that we get over the internet is quite good.

The initial setup was very straightforward.

The initial setup was quite quick, nothing much to do. Now, we are more developing the use cases. A use case with data generally takes around four or five days a use case because it will start right from identifying the right field, getting the data, transforming it, and finalizing the warehouse structure. That makes a bit of a thing, but it's pretty straightforward.

We are a technical team so we implemented it in-house.

It's a pay-as-you-go module. I'm not very sure about cost because our usage currently is very low. But, I feel that if the usage extends beyond a certain threshold, it will start getting expensive.

It depends what the threshold is. I see we're not at that threshold right now, so it's pretty decent right now.

We were looking at certain other projects and products. For example, we were looking at Snowflake that has a data warehouse. But the project wasn't working. That's why we selected Azure. The primary reason is the skills are very easily available for Azure. The second is from our strategy perspective, because we were trying to be a Microsoft shop it fits into our strategy. That's all.

If you're a Microsoft shop, if you want to get there easily, I think Azure is one of the better choices. Otherwise, other tools generally require specialized skills and specialized partners to come and implement it. Once implemented, then it becomes much easier to install.

I can't comment right now. I've not talked to it in that fashion. Whatever was required by us, business users have been satisfied in the Data Factory setup.

On a scale of one to ten, I would give Azure Data Factory 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 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.

We are using this solution to gather information from SCADA systems, analyze it using AI and machine learning, and then sending the results to our users. They receive and view the data using the Power BI interface.

The integration with the whole environment is smooth and transparent.

The reason that we implemented this product is for the full integration with the whole Azure environment. It is very important because you don't have to deal with security, identity flow, or audit flow.

The security of the agent that is installed on-premises is very good. This is another important feature for us.

It comes with a lot of connectors out of the box, including support for SQL, Oracle, SAP, and ODBC.

The pricing scheme is very complex and difficult to understand. Analyzing it upfront is impossible, so we just decided to start using it and figure out the costs on a weekly or monthly basis.

We have just begun using Azure Data Factory within the past two or three months and it is not fully rolled out yet.

We have no issues with technical support. We are working closely with Microsoft partners. There were a couple of problems during the installation that were solved on the fly, and otherwise, we haven't had and trouble.

We started with iConduct and as we migrated to the Azure environment, we began the process of switching to the Microsoft Azure Data Factory.

The initial setup was not very complex and the deployment was very quick. We were set up within a matter of days, or perhaps a week.

We received help from one of the Microsoft partners, who handled the installation for us.

To this point, we are still learning the system and have only tried a very simple data flow. At this point, we haven't had any issues.

I would rate this solution an eight out of ten.

I primarily use the solution for my small and medium-sized clients.

So far, I'm quite happy with the solution overall. It's got a lot of tools that I use in my work, and these are items I'm already recommending to my clients. I'm quite happy with it.

I'm not sure if I have any complaints about the solution at the moment. There are a few bits and pieces that we would like to see improved. These include improvements related to the solution's ease of use and some quality flash upgrades. However, these are minor complaints. 

If the user interface was more user friendly and there was better error feedback, it would be helpful.

I've been working with the solution for three to four years now.

On the tool itself, we've never experienced any bugs or glitches. There haven't been crashes. Stability has been good.

I haven't had the ability to scale any of my projects personally. I also wouldn't need to scale too high if I did. I'm not sure if I can speak to aspects of scalability as I've never dealt with it.

I've contacted Microsoft technical support in the past. I have to say that I've had a good experience with them. I've been quite satisfied with their level of service.

The initial setup was very straightforward. I wouldn't describe it as complex.

Deployment is fast. It only takes about half an hour at most.

I don't have any information about the pricing. I don't deal with that aspect of the solution.

I typically work with small to medium-sized companies. I'm a consultant, so I give different advice based on what my clients need and what they want to do. However, I would recommend this product. 

I'd rate the solution eight out of ten. All of the issues I have with the solution are very minor, however, it means the solution isn't exactly perfect just yet.

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.