Azure Data Factory Reviews

I primarily use Data Factory for creating pipelines on cloud in terms of integrating multiple cloud services.

Data Factory's best feature is the ease of setting up pipelines for data and cloud integrations. It also has good integration with other Azure services.

Data Factory could be improved in terms of data transformations by adding more metadata extractions.

I've been using Data Factory for five years.

Data Factory's stability has improved following some initial issues.

Data Factory's scalability is good.

The initial setup was easy as it's a SaaS offering.

Data Factory is affordable.

I would give Data Factory a rating of eight 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 primarily use Data Factory for data ingestion and B2B transformation.

Data Factory's best features are connectivity with different tools and focusing data ingestion using pipeline copy data.

Data Factory's monitorability could be better. In the next release, Data Factory should include integrations with open-source tools like Air Flow.

I've been working with Data Factory for about a year.

Data Factory is stable.

Data Factory is scalable.

Microsoft's technical support is good, so long as your company has a good relationship with them.

I previously worked with Talend, Matillion, and Fivetran.

Data Factory is expensive.

I would rate Data Factory seven out of ten.

We are using this solution to gather information from SCADA systems, analyze it using AI and machine learning, and then sending the results to our users. They receive and view the data using the Power BI interface.

The integration with the whole environment is smooth and transparent.

The reason that we implemented this product is for the full integration with the whole Azure environment. It is very important because you don't have to deal with security, identity flow, or audit flow.

The security of the agent that is installed on-premises is very good. This is another important feature for us.

It comes with a lot of connectors out of the box, including support for SQL, Oracle, SAP, and ODBC.

The pricing scheme is very complex and difficult to understand. Analyzing it upfront is impossible, so we just decided to start using it and figure out the costs on a weekly or monthly basis.

We have just begun using Azure Data Factory within the past two or three months and it is not fully rolled out yet.

We have no issues with technical support. We are working closely with Microsoft partners. There were a couple of problems during the installation that were solved on the fly, and otherwise, we haven't had and trouble.

We started with iConduct and as we migrated to the Azure environment, we began the process of switching to the Microsoft Azure Data Factory.

The initial setup was not very complex and the deployment was very quick. We were set up within a matter of days, or perhaps a week.

We received help from one of the Microsoft partners, who handled the installation for us.

To this point, we are still learning the system and have only tried a very simple data flow. At this point, we haven't had any issues.

I would rate this solution an eight out of ten.

The use cases are more related to logistics, our finance, and back-office activity.  

The most valuable part of this product is the ease of use. It is easy to use and rather intuitive. Because it is easy to use, you can do things with it easily. The more things make your work easy, the more valuable they are.  

Because I have not really done a really deep benchmark against competitors, I may not be familiar enough with the potential of competing products and capabilities to be able able to say what is missing or should be improved definitively.  

From my perspective, the pricing seems like it could be more user-friendly. Of course, nothing is ever as inexpensive as you want.  

Perhaps one good additional feature would be incorporating more ways to import and export data. It would be nice to have the product fit our service orchestration platform better to make the transfer more fluid.  

We started using this product a year ago.  

The stability of the product is good.  

The scalability seems okay. As we have only been using it for a short time, it is hard to say more. We are not currently planning to scale usage dramatically at this point but of course we would like to grow. On a scale from one to ten and from what I know, I would say scalability is an eight-out-of-ten. I can't be sure exactly how many people are using the system, but we have hundreds of thousands of users currently. Internally, I would say we use the product often.  

I have not had a reason to be in touch with technical support, but I don't know whether others in the organization have been in touch with them. As far as I know, there has been no reason to be.  

The initial setup was not simple and it was not complex. It was in the middle.  

I would say it took two months for the deployment.  

We have a department of developers that implemented the product. The deployment happened before I joined the organization.  

The advice I would give to someone who is looking to implement this product is to understand the IT technology of the product first and why it would be needed. That is the point where you have to start. Next, you have to understand if the product itself fits your organizational needs. That is you have to look at the business requirements and see whether the product really fits the organization and solves the problems while conforming to the business model.  

On a scale from one to ten where one is the worst and ten is the best, I would rate the product overall as an eight-out-of-ten.  

The primary use case of this solution is for data integration.

It has built-in connectors for more than 100 sources and onboarding data from many different sources to the cloud environment.

It has everything we needed.

Being able to post your feedback and queries is a very good feature that is offered by Azure Data Factory.

Understanding the pricing model for Data Factory is quite complex. It needs to be simplified, and easier to understand.

We have experienced some issues with the integration. This is an area that needs improvement.

I have been using this solution for almost two years.

So far it's been stable. We have not experienced any issues.

I haven't put much thought into scalability.

I have contacted technical support.

If there are any queries, they have the provision to post them in the Data Factory GUI and we can contact the team directly. This is a very nice feature.

Any feedback or improvements that we post is acknowledged and taken care of.

The initial setup was straightforward. It was not complex.

Pricing is quite complex.

I have had feedback from many people and many of my team members, and they say that it is difficult to understand. 

Understanding the pricing of Data Factory is quite complex. 

It is not presented in a straightforward way.

I would definitely recommend this solution to anyone who is interested in using it.

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

We generally implement this product for data transformation for our clients. We create the pipelines and provide training before handing it over to them. We generally deal with large-scale organizations. I'm a senior solutions architect. 

I think the main benefit of this solution is the ease of use, especially for companies that have come from an SSIS type of background where they are used to Microsoft tools. 

If you have a very simple pipeline you can use Data Factory for transformations, but it's really for serious analytics. This is an excellent tool for pipeline orchestration; connecting the different components and activities as well as gathering data. It's an orchestration tool, not a transformation tool. The monitoring feature has drastically improved.

Data Factory is embedded in the new Synapse Analytics. The problem is if you're using the core Data Factory, you can't call a notebook within Synapse. It's possible to call Databricks from Data Factory, but not the Spark notebook and I don't understand the reason for that restriction. To my mind, the solution needs to be more connectable to its own services.

There is a list of features I'd like to see in the next release, most of them related to oversight and security. AWS has a lake builder, which basically enforces the whole oversight concept from the start of your pipeline but unfortunately Microsoft hasn't yet implemented a similar feature.

I've been using this solution for five years. 

From what I've seen this is a stable solution. 

The solution is easy to scale keeping in mind that Data Factory doesn't do any computations. We use it mainly to push the computations to Databricks or Synapse. Projects with our clients generally last a few months and only until they go into production. I believe the ability to increase is always there.

We typically do not use customer support, but there were a few cases several years ago as the product was moving to the cloud that things were not so stable and we contacted support services - they were very good. 

When I first started in this field, everything was basically Hadoop on-premise and Hadoop infrastructure. With the increase in cloud integrations, things have changed. Once the big data services got introduced, we were probably one of the few companies in North America that were actually into analytics and big data and we were the first to implement related Microsoft products in Canada.

The initial setup is straightforward. I'm a huge fan and user of CI/CD pipelines and never do deployments manually. It's all automated and deployment takes a few minutes.

Licensing costs of Data Factory are reasonable. The cost is mainly on the Synapse and Databricks side of things because they are the tools where the computations are done and where you need more nodes and servers.

It's important to study the solution before purchasing it. The problem in this market is that because most users are generally not very knowledgeable, they typically fall for services that are not compatible with their use case. Data Factory comes with all the transformations but that doesn't work for serious analytics customers who generally need to resort to Databricks or Synapse which involves training and education. Since it's a new field and everything has just blasted off, it's very hard for people to catch on.

In my opinion, Airflow still ranks as number one but I would rate Data Factory an eight out of 10. 

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.

Positive

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