Azure Data Factory Reviews

Azure Data Factory can be deployed on the cloud and hybrid cloud. There have been very few deployments on private clouds.

The most valuable feature of Azure Data Factory is the core features that help you through the whole Azure pipeline or value chain.

Across the whole field of use, from accepting the ingestion and real-time SaaS ingestion for which we often use other components. These areas have been instrumental across the board.

Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities. This is something that is just not fully covered. Additionally, the integration could improve for other tools, such as Azure Data Catalog.

I have been using Azure Data Factory for approximately four years.

The stability of Azure Data Factory is good.

I rate the scalability of Azure Data Factory a seven out of ten.

Azure Data Factory is scalable. The solution can move up and be aligned to resources or scaled down.

We have a lot of customers using the solution, approximately 100.

The support from Azure Data Factory is very good. There are some improvements needed.

I rate the support from Azure Data Factory a four out of five.

Positive

I have previously used Informatica. When comparing Informatica to Azure Data Factory, Informatica is a bit behind.

The initial setup of Azure Data Factory is not complex if you know what you are doing. If you do not know the technology you will have a problem.

Azure Data Factory gives better value for the price than other solutions such as Informatica.

I recommend this solution to others.

I rate Azure Data Factory an eight out of ten.

We use Azure Data Factory for data transformation, normalization, bulk uploads, data stores, and other ETL-related tasks.

Azure Data Factory allows us to create data analytic stores in a secure manner, run machine learning on our data, and easily adapt to changing schema.

The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring.

The documentation could be improved. They require more detailed error reporting, data normalization tools, easier connectivity to other services, more data services, and greater compatibility with other commonly used schemas.

I would like to see a better understanding of other common schemas, as well as a simplification of some of the more complex data normalization and standardization issues.

It would be helpful to have visibility, or better debugging, and see parts of the process as they cycle through, to get a better sense of what is and isn't working.

It's essentially just a black box. There is some monitoring that can be done, but when something goes wrong, even simple fixes are difficult to troubleshoot.

I have been working with Azure Data Factory for a couple of years.

There is only one version.

Overall, I believe the stability has been good, but there have been a couple of occasions when Microsoft's resources needed to be allocated were overburdened, and we had to wait for unacceptable amounts of time to get our slot. It has now happened twice which is not ideal.

There is no limit to scalability.

We only have a few users. One is a data scientist, and the other is a data analyst.

We use it to push up various dashboards and reports, it's a transitional product for transferring, transforming, and transitioning data.

It is extensively used, and we intend to expand our use.

You don't really get that kind of support; it's more about documentation and the community support that is available. I would rate it a three out of five compared to others.

You could call them, and pay for their consulting hours directly, but for the most part, we try to figure it out or look through documentation. 

I think their documentation is lagging because it's not as popular of a tool, there's just not a lot, or as much to fall back on.

Neutral

We had only our own tools, and we switched because you get to leverage all of the work done in a SaaS or platform as a service, or however they classify it. As a result, you get more functionality, faster, for less money.

The initial setup is straightforward.

It is a working tool. You can start using it within an hour and then make changes as needed.

We only need one person to maintain the solution; it doesn't take much to keep it running.

It's not a problem; it's a platform.

We completed the deployment ourselves.

We have seen a return on investment. I can't really share many details, but for us, this becomes something that we sell back to our clients.

You pay based on your workload. Depending on how much data you process through it, the cost could range from a few hundred dollars to tens of thousands of dollars.

Pricing is comparable, it's somewhere in the middle.

There are no additional fees to the standard licensing fee.

We looked at some other tools, such as Databricks, AmazonGlue, and MuleSoft.

We already had most of our infrastructure connected to Azure in some way. So the integration of where our data resided appeared to be simpler and safer.

I believe it would be beneficial if they could find someone experienced in some of the tools that are a part of this, such as Spark, not necessarily Data Factory specifically, but some of those other tools that will be very familiar and have a very quick time for productivity. If you're used to doing things in a different way, it may take some time because there isn't as much documentation and community support as there is for some more popular tools.

I would rate Azure Data Factory a seven out of ten.

For developers that are very accustomed to the Microsoft development studio, it's very easy for them to complete end-to-end data integration.

One of the features that still is in development is data privacy to the cloud side of the SAP integration.

I have been using Azure Data Factory for 3 years. 

I rate the stability a 9 out of 10. 

6 developers are using the solution at present. 

The initial setup is not very straightforward. I rate it a seven out of ten. 

The pricing is a bit on the higher end. 

Overall, I rate the solution an 8 out of 10. 

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. 

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.

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. 

My clients use Data Factory to exchange information between the on-premises environment and the cloud. Data Factory moves the data, and we use other solutions like Databricks to transform and clean up the data. My teams typically consist of three or four data engineers.

I like how you can create your own pipeline in your space and reuse those creations. You can collaborate with other people who want to use your code.

DataStage is easier to learn than Data Factory because it's more visual. Data Factory has some drag-and-drop options, but it's not as intuitive as DataStage. It would be better if they added more drag-and-drop features. You can start using DataStage without knowing the code. You don't need to learn how the code works before using the solution.

I think the communication about the ADA's would be interesting to see in the platform. How to interact with those kind of information and use it on your pipelines.

I have used Data Factory for eight months.

I have never experienced downtime with Data Factory. 

It isn't that expensive to scale Data Factory up. My client can ask for more resources on the tool, and paying more is never an issue. 

I rate Azure support seven or eight out of 10. There is room for improvement. Sometimes, you don't know where the errors originate. You have to send a ticket to Azure, and they take two or three days to respond. The issue may resolve itself by then. The problem is fixed, but you don't know how to prevent it or what to do if it happens in the future. 

The data transfer has stopped a few times for unknown reasons. We don't know if the resources are insufficient or if there is a problem with the platform. By the time we hear back from Microsoft, the issue has been resolved.

Positive

Data Factory is effortless to set up.

I rate Azure Data Factory nine out of 10. When implementing Data Factory, you should document where you are building so you can pass that information. Sometimes you build something for a specific purpose, but you can use that information for other solutions. If you have a community where you are building things, you can reuse them on the platform, so don't need to build everything from scratch.