Azure Data Factory Reviews

Azure Data Factory allows us to provide BI service. We pull the data and put it into Synapse. From there, we create our dimension fact tables that are being used for reporting. 

The most valuable feature of Azure Data Factory is the improved flexibility compared to SSIS that we previously used for our ETL transformation. I also enjoy the ease of use for the backend JSON generator, the deployment solution, and the template management. All we need to do is create ARM templates.

Microsoft is constantly upgrading its product. Changes can happen every week. Every time you open Data Factory you see something new and need to study what it is. I would like to be informed about the changes ahead of time, so we are aware of what's coming.

In future releases, I would like to see Azure Data Factory simplify how the information of logs is presented. Currently, you need to do a lot of clicks and go through steps to find out what happened. It takes too much time. The log needs to be more user-friendly.

I have been using Azure Data Factory for two years.

Scalability depends on the use case. 

As far as customer service and support with Azure Data Factory, we are not always satisfied with the response time. However, once they attend to the issue, everything is good.

Positive

All we needed to do was create ARM templates and deployment is easy.

We deployed in-house. For deployment, we use ARM templates that are a part of Azure's deployment strategy. It's not only available for Data Factory, it is built in. It links with DevOps, then the ICD integration.

Azure Data Factory is a good tool. Given that the data platform ecosystem is provided by Microsoft, you know it is good.

I would rate the solution an eight out of ten overall.

I'm not sure how much information I can provide regarding to some kind of security of my company, but I can tell you that we were migrating integrations from from platform to the other, and the other platform was error data factory.

For me, it was that there are dedicated connectors for different targets or sources, different data sources. For example, there is direct connector to Salesforce, Oracle Service Cloud, etcetera, and that was really helpful.

It's a good idea to take a Microsoft course. Because they are really helpful when you start from your journey with Data Factory.

I have been using Azure Data Factory for one year. 

The solution doesn't have stability issues. 

It is easy to scale. 

The initial setup is straightforward. Deployment is automatic and takes few minutes. 

Overall, I would rate it an eight out of ten. 

We had an old, traditional data warehouse. We decided to put it into the cloud and we used Azure Data Factory to reform the EEL process from SQL server integration services to extra data.

Azure Data Factory was chosen by the team that I was not on at the time and who decided that this would be the move to the future. So I just went along with it.

In terms of my personal experience, it works fine.

We didn't have a very good experience. The first steps were very easy but it turned out that we used Europe for a Microsoft data center, also partly abroad for our alpha notes. As soon as we started using Azure Data Factory, the bills got higher and higher. At first we couldn't understand why, but it is very expensive to put data into a data center abroad. So instead, we decided to use only Northern Europe, which worked out for a while in the beginning. And then we had nothing to show for it. They gave me a really hard time for this.

Azure Data Factory should be cheaper to move data to a data center abroad for calamities in case of disasters.

What I really miss is the integration of Microsoft TED quality services and Microsoft Data services. If they were to combine those features in Data Factory, I think they would have a very strong proposition. They promise something like that on Microsoft Congress. That was years ago and it's still not here.

I have been using Azure Data Factory for a couple of years now, since about 2017.

Azure Data Factory is a stable solution.

Yes, Azure Data Factory is scalable.

We previously used SSIS because of Microsoft.

The installation is straightforward.

We use data engineers to do the install.

We pay monthly for this.

On a scale of one to ten, I would give Azure Data Factory a seven. Compared to Informatica, it's really crude. I think it's a very crude solution. 

Would I recommend Azure Data Factory? It depends if they need a straight reading in data, then I would say it's perfect. But with Informatica, you can do data storing and data quality checks - there is a lot there than just a data center.

I think Azure Data Factory is a mature product. We used Version One in my project and a lot of it isn't possible on this version. The Version Two is much faster and much better. It's not at the same level as Informatica.

My task involves extracting data from a source, performing necessary transformations, and subsequently loading the data into a target destination, which happens to be Azure SQL Database.

The company is experiencing significant benefits as one of our customers is successfully implementing the solution we provide. We offer support to the customer in utilizing Azure Data Factory, and their satisfaction level is quite high.

The most valuable aspect is the copy capability.

Implementing a standard pricing model at a more affordable rate could make it accessible to a larger number of companies. Currently, smaller businesses face a disadvantage in terms of pricing, and reducing costs could address this issue.

I have been working with it for more than six months.

The stability is quite satisfactory. I would rate it nine out of ten.

It provides impressive scalability. There are a total of eight switches currently in use. I would rate it nine out of ten.

Technical support is available for all products, and you can reach them without any hassle. They offer assistance through various channels and are proficient in addressing technical issues, ensuring the highest level of support. I would rate it nine out of ten.

Positive

In the past, we used Oracle Data Integrator.

The initial setup has been smooth and without any difficulties.

Deployment time is contingent on the volume of data. If there are millions of records to process, it will understandably take a significant amount of time. Conversely, for smaller datasets, the deployment can be relatively quick, often within a matter of minutes to reach the destination. In the cloud deployment process, the initial step involves defining the instance. Subsequently, in the copy activity, we specify both the source and destination. Following this, communication with the destination takes place. This process constitutes the necessary steps for deployment, and we proceed accordingly. Due to the cloud system being provided by Microsoft, they handle the maintenance of the servers.

The routing value is quite impressive, especially considering that we successfully implemented it for one of our clients, and they expressed satisfaction. For our latest projects, we've acquired additional customers who also require this product for their setups. I would rate it eight out of ten.

While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products.

I would recommend considering this solution because, from my perspective, it is not overly expensive. The pricing seems reasonable, making it a viable option to explore. Overall, I would rate it nine out of ten.

Our company uses the solution to extract, transform, and load the data into the database.

Feature-wise, one of the most valuable ones is the data flows introduced recently in the solution.

Currently, our company requires a monitoring tool, and that isn't available in Azure Data Factory. Although the platform displays which pipelines are running, it doesn't offer a monitoring tool that allows for the sequential execution of pipelines and the ability to visualize end-to-end data flow. As such, this feature is currently missing from the platform.

I have been using Azure Data Factory for more than six years. Also, I am an end-user of the solution, and I initially used to work on Azure Data Factory V1. Now, I have switched to Azure Data Factory V2.

It is a stable solution. Stability-wise, I rate the solution an eight or nine out of ten.

Scalability-wise, I rate the solution a seven or eight out of ten. So, scalability can be improved. Also, there are around 150 people in my company using the solution. Moreover, we use the solution daily in our company.

I rate the technical support between eight to nine out of ten.

Positive

Previously, I was using Informatica. My company wanted to shift to a solution that could be deployed on the cloud, so we chose Azure Data Factory.

The solution's initial setup process was easy. On a scale where one is difficult and ten is easy, I rate the initial setup process an eight out of ten. The solution is deployed on the cloud.

Since multiple projects are going on in my organization, there is no uniformity in the time taken to deploy the solution in our company. However, I can say that it only takes a few days to carry out the deployment process.

Our organization has multiple project teams, so each team carries out its deployment process.

To give an average, I would consider that if there are fifty ongoing projects in our company, and if we consider a person from each project, fifty people are needed for the deployment and maintenance process.

The solution's implementation process was done with our in-house team's help.

I cannot comment on the pricing parts since our company's admin team handles it.

Those who want to move to a cloud platform can choose Azure Data Factory since it is the best tool. Since certain improvements are required in the solution, I rate the overall solution an eight out of ten.

There was a need to bring a lot of CRM and marketing data for some PNL analysis. We are connecting to the Salesforce cloud. In it, there's a specific solution in Salesforce Core CRM for the pharmaceutical industry. We are using the solution to connect to that and we are bringing in the various dimensions and transactions from that data source.

Data Factory itself is great. It's pretty straightforward. You can easily add sources, join and lookup information, etc. The ease of use is pretty good. 

They have a lot of other components like a newer monitor, which helps track and generate alerts for any failed jobs and things of that nature, which is helpful.

At this point in time, they should work on somehow integrating the big data capabilities within it. I have not explored it, but it would be good if somehow we could call a Spark job or something to do with the Spark SQL within ADS so that we wouldn't need a Spark tested outside.

On the UI side, they could make it a little more intuitive in terms of how to add the radius components. Somebody who has been working with tools like Informatica or DataStage gets very used to how the UI looks and feels. 

In ADS, adding a new table or joining a new table and overriding that with an override SQL that I could customize would be helpful.

Being able to debug from the design mode itself would be helpful.

I've been using the solution for one year.

In the latest version, the v2 version, the solution is pretty stable. It does not give unknown letters or things like that.

The solution allows you to create reusable components, so it can be scaled pretty easily.

Being an IT services company, we have a gold or a platinum partnership with Microsoft. For us, getting the technical support we need is not a big issue. Their community is also pretty active in responding to any issues. It's quite good. We've been satisfied with the level of support that is offered.

We were not actually involved in the initial setup. That was all with the client, so I won't be able to comment on it.

In terms of licensing costs, we pay somewhere around S14,000 USD per month. There are some additional costs. For example, we would have to subscribe to some additional computing and for elasticity, but they are minimal. 

For disaster recovery and readability setups, we did that on Data Lake.

We use the public cloud deployment model.

I'd warn others to ensure that the design should be frozen before you start building because overriding each other's code and managing code takes effort. To avoid or to reduce that effort, ensure that the design is frozen. You can build some configurable code rather than hard-coding everything into the jobs. That's the biggest recommendation.

I'd rate the solution seven out of ten. It's a pretty good solution, but over the past year, I've been limited on the number of cases I have on it. If it had a better user interface and was more intuitive I would have rated it higher.

We primarily use the solution for data warehousing. I'm integrating data from multiple sources.

I have a team helping me out from India. It helps manage the data and collect everything from sources.

The product has a lot of features.

It makes it easy to collect data from different sources.

The solution offers good integration with pretty much any system.

I like that it is cloud-based. 

Sometimes when I run some jobs, I have issues with the log flow. I want to see where the data goes. I want to see the data stream.

I'd like more integrations with other APIs. Sometimes I need to do some coding, and I'd like to avoid that. I'd like no-code integrations. 

I've been using the solution for four or five years. 

I'm using the solution as an end-user.

We are using the latest version of the solution. 

I'd rate the solution seven out of ten.

Our primary use case is for traditional ETL; moving data from the web and data sources to a data warehouse. I've also used Data Factory for batch processing and recently for streaming data sets. We have a partnership with Microsoft and I am a cloud solution architect.

There are a lot of innovative features that Microsoft releases regularly. The UI is easy to navigate and finding information on new features has proven to be quite easy. I like that I can retrieve VTL code pretty quickly without knowing in-depth coding languages like Python. Microsoft has very good support teams that I've dealt with and they are very helpful in resolving problems.

Improvement could be made around streaming data because I feel that the Data Factory product is mainly geared for batch processing and doesn't yet have in-built streaming data processing. Perhaps it's on the way and they are making some changes to help facilitate that. If they were to include better monitoring that would be useful. I'd like to see improved notifications of what the actual errors are.

I've been using this solution for four years. 

The solution is stable. 

The way we've used the product is around streaming data, and that doesn't work well with Data Factory. As loads increase, some of the underlying infrastructure that gets used to process data seems to slow down. This is basically a development product, so in terms of scalability it doesn't have a wide user base, it's only meant for developers and analysts. The number of users will vary from anywhere between one to five people. 

The customer service is excellent. 

I've previously used Microsoft SSIS, which is the last incarnation of Azure Data Factory. I've also used IBM Data Manager and Teradata's Management Loads, so I have quite a bit of experience in this area. One of the major differences is that Azure Data Factory is a SaaS service. The other solutions were in-house and you managed your own infrastructure to run them. What I get from Data Factory is a lot better than what I got for the other products. Azure Data Factory is great because it's evolving day-to-day.

The initial setup is relatively straightforward. That's assuming that when you're creating the Data Factory, you have some knowledge about how to create it. We deployed in-house and it took about ten minutes for the initial creation process. The provisioning itself is not something you have control over because it's a self-service that does what it needs and happens in the background. The infrastructure doesn't require any maintenance, it's all managed on the backend. There is maintenance in terms of your codes, connections, and the like, but that is separate from the infrastructure maintenance.

The Data Factory itself will not give you a return on investment, it's the entire solution that brings a return, I'd say at least 20% to 30% of return for investment over a five-year period. 

Azure Data factory is a pay-as-you-go service so cost depends on the number of connections, how many times each activity in that node is run, and how much data gets moved. There are a number of factors that define the price, but it pairs with your service. You're charged for the amount of data that's moved, but there are no charges for the features you use.

I would definitely recommend this solution. If you decide to implement Data Factory, I suggest reaching out to qualified professionals because there are a lot of moving parts. That said, if you have internally qualified staff, deployment shouldn't be a problem. Apart from a few minor issues, it's pretty reliable with good support and a whole bunch of resources available on the web.

I rate this solution a solid nine out of 10.