Azure Data Factory Reviews

Our primary use case is mainly for ETL and transforming the data and then using it for power VA. So there we are handling multiple projects. It is not just a single thing, but mainly used at the end is data analytics only.

I would like to see this time travel feature in Snowflake added to Azure Data Factory. In addition to taking care of data internally, how it is instead of using indexing, they have some different mechanisms to quickly execute the query instead of normal indexes. Both of these would be great to see implemented in future upgrades.

I have been working with Azure Data Factory for the past twelve months.

I am one hundred percent happy with the stability.

Technical support is excellent.

I would rate Azure Data Factory an eight on a scale of one to ten.

Data Factory's best features include its data source connections, GUI for building data pipelines, and target loading within Azure. It's also highly configurable and integrates well with the rest of the Azure services.

Data Factory's performance during heavy data processing isn't great.

Data Factory is stable - I have customers running thousands of jobs a day without problems.

Data Factory is scalable.

Microsoft's technical support is pretty good.

The initial setup is complex because there are a lot of prerequisites, including plumbing in the network, but that's typical for any cloud-based solution.

Data Factory is a good, mature solution, and I would rate it as eight out of ten.

We mainly use this solution to carry out data movement and transformation.

This solution has provided us with an easier, and more efficient way to carry out data migration tasks.

This solution is currently only useful for basic data movement and file extractions, which we would like to see developed to handle more complex data transformations.

We have been using this solution for a year.

We have found this to be a stable solution.

This is an easily scalable product, due to it being cloud-based.

The customer support for this solution is very good. 

The initial setup of this product is straightforward, if you deploy the solution using a template; rather than implementing the solution first, and configuring the features afterwards.

We implemented the product using both in-house staff, and members of a vendor team. The vendor team were very helpful, and gave good advice while we were deploying the solution.

We would recommend this solution as it is very solid and has good security features.

I would rate this solution an eight out of 10.

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. 

We use Azure Data Factory to connect to clients' on-premise networks and data sources to bring the data into Azure. Additionally, Azure Data Factory orchestrates data movement and transformations. It can connect to a number of different cloud data sources to bring the information into something, such as a data lake or a formal SQL database. Azure Data Factory has the ability to handle large data workloads and can orchestrate them well.

The most valuable features of Azure Data Factory are the flexibility, ability to move data at scale, and the integrations with different Azure components.

Azure Data Factory can improve by having support in the drivers for change data capture.

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

Azure Data Factory is a very reliable and stable solution.

The solution is highly scalable.

The technical support is very good, they are responsive.

We previously use Attunity and we switch to Azure Data Factory mainly because of cost reasons and integration.

The biggest difference between Azure Data Factory and Attunity is Attunitys has the ability to perform change data capture. Whereas Azure Data Factory is more centered around batch or bulk loads.

The initial setup is of a moderate level of difficulty. However, it can be complex. The solution is able to fit both of our use cases.

We normally use one or two people to update and maintain Azure Data Factory.

There's no licensing for Azure Data Factory, they have a consumption payment model. How often you are running the service and how long that service takes to run. The price can be approximately $500 to $1,000 per month but depends on the scaling.

My advice to others that want to implement Azure Data Factory is to use a metadata approach.

I rate Azure Data Factory an eight out of ten.

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 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.

In my new organization, we are currently analyzing this solution. In my previous organization, I have worked with Azure Data Factory and Databricks.

It is deployed in a virtual private cloud.

Its integrability with the rest of the activities on Azure is most valuable.

It is also easier to work with. It has a nice user interface that would seem very familiar to some of the people who are coming from Microsoft SSIS and other similar kinds of activities. It makes it easy for them to pick it up quickly.

It can improve from the perspective of active logging. It can provide active logging information.

It should provide support for changing data capture on several other platforms.

I have been using this solution for two years.

It is scalable. From the security perspective, it depends on how we implement it. It depends on the organization, but I don't see a challenge. 

In my previous organization, we had close to a hundred people.

Their technical support is very good.

It was very easy.

I would generally recommend this solution if you have an Azure setup or Azure as a baseline.

I would rate Azure Data Factory an eight out of 10.

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. 

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.