Azure Data Factory Reviews

We are currently using it as an ETL (Extract, Transform, and Load) tool. We are using it to connect to various information providers or, in general, to various sources, to extract data, and then to insert it to our storage devices, databases, or data warehouses.

The two most valuable features of Azure Data Factory are that it's very scalable and that it's also highly reliable.

One area for improvement is documentation. At present, there isn't enough documentation on how to use Azure Data Factory in certain conditions. It would be good to have documentation on the various use cases.

Sometimes, it's really difficult to find the answers to very technical questions regarding certain conditions.

I've been using Azure Data Factory since 2019.

It has been stable so far.  

Azure Data Factory is a very scalable solution. Including internal developers and external consultants working for us, we have about 10-15 people using this solution.

We had been using various ETL tools during the years before moving to the cloud. We picked Azure Data Factory because we were moving towards the Azure cloud.

The initial setup is very easy.

We used a consultant as it was a big project. We had five to six specialists, including both internal and external employees, working on it. It took about about three to six months to complete.

Azure Data Factory is a very easy to use tool. If you want to extract, manipulate, and load data to any type of Azure repository, I recommend this solution. However, I would not recommend it if the manipulation of data is very deep and complicated.

I would rate this solution at eight on a scale from one to ten.

When it comes to our business requirements, this solution has worked well for us. However, we have not stretched it to the limit. We use this solution to collect data from multiple data sources and store it in the cloud. We are using it every day and sometimes multiple times a day. 

We use this solution within a limited context, specifically for extracting data and moving it to the Azure Cloud to develop our BI solution. Based on our usage, we have not found any challenges using the solution but have not explored every feature. The one element of the solution that we have used and could be improved is the user interface. 

I have used this solution for four years. 

This is a stable solution. 

We have not had a reason to reach out to the support team. The documentation that they provide has been good enough for our technical team to work out the solution that we needed to use.

I would advise future users of this solution to have a clear definition of their use case. In my experience in certain contexts, PBI worked great for us but we did have concerns around security. The most important thing is to contextualize the use of this tool to work out if it meet the needs of a particular business. 

I would rate this solution a nine out of ten. 

Our primary use case is data loading.

The most valuable features are data transformations.

The speed and performance can be further improved.

This solution should be able to connect with custom APIs.

We have been using this solution for one or two months.

The stability of the Azure Data Factory is very good.

The scalability is good. We have about five users for this solution and they are all developers.

The technical support for this solution is good.

We did not use another solution prior to this one.

The initial setup of the Azure Data Factory was simple. It took us one week to deploy it.

We deployed this solution with our in-house team.

Good

Our licensing fees are approximately 15,000 ($150 USD) per month. There are no additional fees.

We evaluated a solution by Talend before selecting the Azure Data Factory.

My advice for anybody who is implementing this solution is to first get in touch with the Microsoft team to get their support so that you don't spend too much on research.

I would rate this solution a eight out of ten.

Used Azure Data Factory, Data Flow (private preview) and Databricks to develop data integration processes from multiple and varied external software sources to an OLTP application Azure SQL database. The tools are impressively well-integrated, allowing quick development of ETL, big data, data warehousing and machine learning solutions with the flexibility to grow and adapt to changing or enhanced requirements. I can't recommend it highly enough.

This solution will allow the organisation to improve its existing data offerings over time by adding predictive analytics, data sharing via APIs and other enhancements readily. It's as simple as extending the data pipelines with new modules and components. The solution is improving the organisation by offering something the organisation can grow with.

Data Flow and Databricks are going to be extremely valuable services, allowing data solutions to scale as the business grows and new data sources are added.

Data Flow is in the early stages — currently public preview — and it is growing into a tool that will offer everything other ETL tools offer. There are a few features still to come. The thing we missed most was data update, but this is now available as of two weeks ago. A feature that is confirmed as coming soon is the ability to pass in a parameter and filter, etc.

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 use the solution to gather data from four separate operational systems.

The data copy template is a valuable feature. With the pipeline template, it takes only a few clicks for the on-premises data to come in. 

There is no built-in function for automatically adding notifications concerning the progress or outline of a pipeline run. I believe this requires a bit of development through power ups, but it would be nice to have a task for firing off an email through the GUI.

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

When it comes to stability, the solution is similar to the previous Azure Synapse Analytics. They are both very easy.

When it comes to scalability, the solution is similar to the previous Azure Synapse Analytics. They are both very easy.

The licensing cost is included in the Synapse.

I rate Azure Data Factory as an eight out of ten.