Azure Data Factory Reviews

We generally implement this product for data transformation for our clients. We create the pipelines and provide training before handing it over to them. We generally deal with large-scale organizations. I'm a senior solutions architect. 

I think the main benefit of this solution is the ease of use, especially for companies that have come from an SSIS type of background where they are used to Microsoft tools. 

If you have a very simple pipeline you can use Data Factory for transformations, but it's really for serious analytics. This is an excellent tool for pipeline orchestration; connecting the different components and activities as well as gathering data. It's an orchestration tool, not a transformation tool. The monitoring feature has drastically improved.

Data Factory is embedded in the new Synapse Analytics. The problem is if you're using the core Data Factory, you can't call a notebook within Synapse. It's possible to call Databricks from Data Factory, but not the Spark notebook and I don't understand the reason for that restriction. To my mind, the solution needs to be more connectable to its own services.

There is a list of features I'd like to see in the next release, most of them related to oversight and security. AWS has a lake builder, which basically enforces the whole oversight concept from the start of your pipeline but unfortunately Microsoft hasn't yet implemented a similar feature.

I've been using this solution for five years. 

From what I've seen this is a stable solution. 

The solution is easy to scale keeping in mind that Data Factory doesn't do any computations. We use it mainly to push the computations to Databricks or Synapse. Projects with our clients generally last a few months and only until they go into production. I believe the ability to increase is always there.

We typically do not use customer support, but there were a few cases several years ago as the product was moving to the cloud that things were not so stable and we contacted support services - they were very good. 

When I first started in this field, everything was basically Hadoop on-premise and Hadoop infrastructure. With the increase in cloud integrations, things have changed. Once the big data services got introduced, we were probably one of the few companies in North America that were actually into analytics and big data and we were the first to implement related Microsoft products in Canada.

The initial setup is straightforward. I'm a huge fan and user of CI/CD pipelines and never do deployments manually. It's all automated and deployment takes a few minutes.

Licensing costs of Data Factory are reasonable. The cost is mainly on the Synapse and Databricks side of things because they are the tools where the computations are done and where you need more nodes and servers.

It's important to study the solution before purchasing it. The problem in this market is that because most users are generally not very knowledgeable, they typically fall for services that are not compatible with their use case. Data Factory comes with all the transformations but that doesn't work for serious analytics customers who generally need to resort to Databricks or Synapse which involves training and education. Since it's a new field and everything has just blasted off, it's very hard for people to catch on.

In my opinion, Airflow still ranks as number one but I would rate Data Factory an eight out of 10. 

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.

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 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.

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 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 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.

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.