Azure Data Factory Reviews

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.

The solution is primarily used for data integration. We are using it for the data pipelines to get data out of the legacy systems and provide it to the Azure SQL Database. We are using the SQL data source providers mainly.

The data pipeline and the orchestration functionality are the most valuable aspects of the solution.

The interface is very good. It seeks to be very responsive and intuitive.

The initial setup is very quick and easy.

I'm more of a general manager. I don't have any insights in terms of missing features or items of that nature.

Integration of data lineage would be a nice feature in terms of DevOps integration. It would make implementation for a company much easier. I'm not sure if that's already available or not. However, that would be a great feature to add if it isn't already there.

We've used the solution for the last 12 months or so.

From what I have witnessed, the solution is quite stable. It doesn't crash or freeze. There are no bugs or glitches. It's reliable.

We work with medium to enterprise-level organizations. Customers have anywhere from 300 employees up to 160,000 employees.

Microsoft offers a great community. There's a lot of support available. We're quite satisfied with the level of assistance on offer.

Since the solution is a service, it's basically just a click and run setup. It's very simple. There's very little implementation necessary. A company should be able to easily arrange it. The deployment doesn't take very long at all.

We do provide the implementation for our clients. We're able to provide templates as well. We have predefined implementation space in Data Factory and provide it to the customer.

While clients might individually evaluate other options, however, we're not aware of that information. I can't say what other solution clients might consider before ultimately choosing Microsoft. I would say that it is likely Talend and maybe SQL Server Integration Services.

We are like an integrator. We are a data warehouse NPI consulting company and we use Data Factory to pull data from different legacy systems and do all these transformations that are necessary in order to provide analytical models.

In our normal scenario is that we are providing Azure SQL Databases together with Azure Data Factory and Power BI. 80% of our customers have recognized such a scenario.

On a scale from one to ten, I'd rate the solution at an eight. We've been largely happy with the capabilities of the product.

My primary use case is getting data from the sensors.

The sensors are installed on the various equipment across the plant, and this sensor gives us a huge amount of data. Some are captured on a millisecond basis.

What we are able to do is the data into Azure Data Factory, and it has allowed us to scale up well. We are able to utilize that data for our predictive maintenance of the assets of the equipment, as well as the prediction of the breakdown. Specifically, we use the data to look at predictions for future possible breakdowns. At least, that is what we are looking to build towards.

It has helped us to take care of a lot of our analytics requirements. We are running a few analytics models on Data Factory, which is very helpful.

The overall architecture has been very valuable to us. It has allowed us to scale up pretty rapidly. That's something that has been very good for us. 

The solution can scale very easily.

The stability is very good and has improved very much over time.

My only problem is the seamless connectivity with various other databases, for example, SAP. Our transaction data there, all the maintenance data, is maintained in SAP. That seamless connectivity is not there. 

Basically, it could have some specific APIs that allow it to connect to the traditional ERP systems. That'll make it more powerful. With Oracle, it's pretty good at this already. However, when it comes to SAP, SAP has its native applications, which are the way it is written. It's very much AWS with SAP Cloud, so when it comes to Azure, it's difficult to fetch data from SAP.

The initial setup is a bit complex. It's likely a company may need to enlist assistance.

Technical support is lacking in terms of responsiveness.

We've been using the solution roughly for about a year and a half.

It hasn't been an extremely long amount of time. 

From a security perspective, the product has come up a long way.

With the Azure Cloud Platform, in 2015, I was in a different organization and it was not reliable at all. It has become much more reliable since then and is very stable at the moment. It's reliable.

The solution is pretty easy to scale on Azure. I have found it to be very efficient and it is pretty fast. You just need to get the order done properly, and then you will be able to scale up.

We have about five to seven people using it at this time.

Technical support isn't the best, as it's a bit delayed at times.

Whenever we need some urgent support, wherein we have to restart or something has stuck, it takes a bit of time. Some improvements can be made in the customer support area.

In summary, we are not completely satisfied with the support.

The initial setup is not straightforward. It's a bit complex. A company may need to hire someone to assist them with the process.

The solution's deployment took about eight weeks.

I had to hire technical experts who could help us in the process. We could not handle the implementation ourselves.

Cost-wise, it is quite affordable. It's not a factor in the decision-making process when it comes to whether or not we should use it. That said, the pricing is very reasonable.

We evaluated both Oracle and SAP before choosing Azure Data Factory.

We are customers and end-users.

I'd advise companies considering the solution that they need to be very clear about the use case they are trying to address. They need to understand the data ecosystem that they have and what percentage of data is coming in from the various ERP systems.

Do that study properly and then come up with the right solution. If, for example, it is that the underlying data that they want to analyze is more than 60% residing in SAP, then probably Azure would not be the right platform to move ahead with.

We're mostly satisfied with the product. However, getting it connected to closed ERP systems like SAP would make it more powerful.

I would rate the solution eight out of ten.

The primary use case is integrating data from different ERP systems and loading it into Azure Synapse for reporting. We use Power BI for the reporting side of it.

We also have customers who are migrating to Azure Data Factory and we are assisting them with making the transition.

It is easy to integrate.

I do not foresee any issues with security.

I find that Azure Data Factory is still maturing, so there are issues. For example, there are many features missing that you can find in other products.

You cannot use a custom data delimiter, which means that you have problems receiving data in certain formats. For example, there are problems dealing with data that is comma-delimited.

I have been using Azure Data Factor for almost one year.

The stability is dependent on how you set up your cloud infrastructure, and how you authorize people to make use of it.

I have not seen any issues with respect to scalability, as it is all hosted within the cloud. We have approximately 20 users.

I have been in contact with technical support, although most of the time I was told that the feature I was interested in was not yet available. In these cases, they will be implementing the missing features in the future.

I use several similar products by different vendors including Talend, Informatica, and Microsoft SSIS. The biggest advantage that Azure has is deployment. However, in others, it is possible to specify custom data delimiters.

The initial setup is pretty simple and it can be deployed in a couple of hours.

I deployed it myself and am also responsible for maintenance.

I would rate this solution a five out of ten.

I use this primarily for ETL tasks.

The most valuable feature is the ease in which you can create an ETL pipeline.

The GUI is very helpful when it comes to creating pipelines. The user interface is also very fast.

The connection to Snowflake is easy. I can store data and transform it during the ETL process before sending it to Snowflake.

Azure Data Factory is a bit complicated compared to Informatica. There are a lot of connectors that are missing and there are a lot of instances where I need to create a server and install Integration Runtime.

The support and the documentation can be improved.

There are a lot of tasks that you need to write code for.

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

I have experience with Informatica and I find it easier to use. For example, there are a lot of connectors that are directly available. Also, Informatica is able to take incremental copies, but with Azure, you have to write code to do that.

I have also worked with Matillion and Fivetran, and I feel that there are a lot of things that Azure can learn from these products. For example, with Fivetran there are very good connectors for copying data between other solutions. This is unlike Azure, where a lot of the time, I have to build my own logic.

The initial setup is complex.

I would rate this solution a seven out of ten.

The platform simplifies data access and visualization with minimal coding, catering to various data management needs across different client projects.

The product centralizes data workflows, enhancing data integration efficiency and visualization for comprehensive analysis.

The platform excels in data transformation with its user-friendly interface and robust monitoring capabilities, making ETL processes seamless.

The product integration with advanced coding options could cater to users needing more customization.

I've been using Azure Data Factory for almost a year now.

The product is stable. 

The product is scalable. 

The initial setup process is straightforward. 

Azure products generally offer competitive pricing, suitable for diverse budget considerations.

Data Factory integrates seamlessly within the Azure ecosystem, offering robust data management capabilities across large-scale projects.

I rate it a nine out of ten. 

We use the solution to move data from on-premises to the cloud.

The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring.

I have been using the product since 2019.

The tool is a stable product.

I would rate the solution a seven out of ten.

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.