Azure Data Factory Reviews

Azure Data Factory allows us to provide BI service. We pull the data and put it into Synapse. From there, we create our dimension fact tables that are being used for reporting. 

The most valuable feature of Azure Data Factory is the improved flexibility compared to SSIS that we previously used for our ETL transformation. I also enjoy the ease of use for the backend JSON generator, the deployment solution, and the template management. All we need to do is create ARM templates.

Microsoft is constantly upgrading its product. Changes can happen every week. Every time you open Data Factory you see something new and need to study what it is. I would like to be informed about the changes ahead of time, so we are aware of what's coming.

In future releases, I would like to see Azure Data Factory simplify how the information of logs is presented. Currently, you need to do a lot of clicks and go through steps to find out what happened. It takes too much time. The log needs to be more user-friendly.

I have been using Azure Data Factory for two years.

Scalability depends on the use case. 

As far as customer service and support with Azure Data Factory, we are not always satisfied with the response time. However, once they attend to the issue, everything is good.

Positive

All we needed to do was create ARM templates and deployment is easy.

We deployed in-house. For deployment, we use ARM templates that are a part of Azure's deployment strategy. It's not only available for Data Factory, it is built in. It links with DevOps, then the ICD integration.

Azure Data Factory is a good tool. Given that the data platform ecosystem is provided by Microsoft, you know it is good.

I would rate the solution an eight out of ten overall.

Depending on their pipeline, our customers use Azure Data Factory for their ELT or ETL transformation processes.

I like that it's a monolithic data platform. This is why we propose these solutions.

The performance could be better. It would be better if Azure Data Factory could handle a higher load. I have heard that it can get overloaded, and it can't handle it.

We have been providing customers Azure Data Factory solutions for about five years. 

Azure Data Factory is a stable solution. The performance is good.

Microsoft technical support is good. We are a Gold partner, and we have got good tech support from them.

From a cloud perspective, the initial setup is straightforward. We have a data engineering team with about 15 professionals managing and maintaining this solution.

We have an accelerated solution around it. We utilize our accelerated solutions to spin all these services into the cloud. So, for us, it does not take much time.

I would recommend this solution depending on whether they want AWS, Azure, or GCP. We recommend all of them to our customers. We have about 50 to 80 people who are using this solution.

On a scale from one to ten, I would give Azure Data Factory a nine.

Essentially, Azure Data Factory is more aligned to ETL, but I wanted to provide a solution for a full data lake solution where I could leverage functionality, whether it is ETL, data ingestion, data warehousing, or data lake.

I was planning to switch to Synapse and was just looking into Synapse options.

I wanted to plug things in and then put them into Power BI. Basically, I'm planning to shift some data, leveraging the skills I wanted to use Synapse for performance.

I am not a frequent user, and I am not an Azure Data Factory engineer or data engineer. I work as an enterprise architect. Data Factory, in essence, becomes a component of my solution. I see the fitment and plan on using it. It could be Azure Data Factory or Data Lake, but I'm not sure what enhancements it would require.

User-friendliness and user effectiveness are unquestionably important, and it may be a good option here to improve the user experience. However, I believe that more and more sophisticated monitoring would be beneficial.

I work as an enterprise architect, and I have been using Azure Data Factory for more than a year.

I am working with the latest version.

Azure Data Factory is a stable solution.

Azure Data Factory is a scalable product.

In my current company, I have a team of five people, but in my previous organization, there were 20.

Technical support is good. We encountered no technical difficulties. Microsoft supported us when we planned to provision Azure Data Factory over a private link. As a result, we received excellent support from Microsoft.

Products such as Azure Data Factory and Informatica Enterprise Data Catalog were evaluated. This is something I'm working on. I work as an enterprise architect, so these are the tools that I frequently use.

Previously, I worked with SSIS. We did not change. Because we were building a cloud-based ETF solution Azure Data Factory was an option, but when it came to on-premises solutions, the SQL server integrating the SSIS tool was one option.

The initial setup is easy.

It took three to four weeks to get up to speed and get comfortable using it.

Pricing appears to be reasonable in my opinion.

My only advice is that Azure Data Factory, particularly for data ingestion, is a good choice. But if you want to go further and build an entire data lake solution, I believe Synapse, is preferred. In fact, Microsoft is developing and designing it in such a way that, it's an entirely clubbing of data ingestion, and data lake, for all things. They must make a decision: is the solution dedicated to only doing that type of data ingestion, in which case I believe Data Factory is the best option.

I would have preferred, but I'm not a frequent user there right now. I need to think beyond Data Factory as an open-source project to include machines and everything else. As a result, as previously stated, Data Factory becomes very small at the enterprise architect level. I was inundated with power automation, power ops, power virtualizations, and everything else in Microsoft that I had to think about.

I would rate Azure Data Factory a seven 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.

We use this solution to perform ELTs so that we do not need to keep code within a database.

The overall performance is quite good.

Occasionally, there are problems within Microsoft itself that impact the Data Factory and cause it to fail.

I've worked with this solution for two and a half years.

I wouldn't consider it to be stable since it fails at times.

The solution is scalable.

Support is quite slow and they have bugs that they are unaware of and claim that that is how the system is supposed to work.

My company used Informatica PowerCenter in the past but I was not involved in that.

The initial setup was quick and easy. The whole process took about fifteen minutes. We have about a hundred users at the moment and have plans to increase.

Two of our in-house developers were able to complete the setup.

This solution has good performance but could use better stability. I would rate this a nine out of ten.

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.

In my company, we use Azure Data Factory for everything related to data warehousing. Depending on my customer's wants, I will use SSIS or Azure Data Factory. If my customers want Fivetran, I will use it for them. If the customer wants a suggestion from me on what they should use, then I will look at what they have today and their skills. According to the inputs I receive from my customers, I will recommend what makes more sense for a particular customer. I can be called a software agnostic.

I can do everything I want with SSIS and Azure Data Factory.

There aren't many third-party extensions or plugins available in the solution. Adjunction or addition of third-party extensions or plugins to Azure Data Factory can be a great improvement in the tool. Creation of custom codes, custom extensions, or third-party extensions, like Lookup extension, should be made possible in the tool.

I am unsure if Azure Data Factory bridges the gap between on-premises, cloud, and hybrid solutions. I would like to see a version that would work equally well in both on-premises and cloud environments. I would like to see the aforementioned offerings made to customers as valuable alternatives to the old SSIS tool.

I have been using Azure Data Factory for many years. I started using the tool since it was called DTS and then, later, SSIS. I currently use Microsoft SQL SSIS 2019.

The solution is deployed on the cloud.

Overall, I rate the solution an eight out of ten.

Our company uses the solution for data ingestion. 

It is beneficial that the solution is written with Spark as the back end. 

The solution is cloud-based and integrates well with other Azure products such as Synapse Analytics.  

There are limitations when processing more than one GD file. 

Data ingestion pipelines sometimes fail because of transient issues that have to do with the cloud network. It takes more than six hours to process or ingest 300,000 records and that is a long time. 

I have been using the solution for two years. 

The solution is new in the market and pretty stable because ADF is a little more codified than AWS. Synapse Analytics adds another tool for data. 

Stability is not quite at the level of Informatica or DataStage. 

The solution is scalable. 

For multi-tenant applications connected to multiple databases, Microsoft recommends a share box and a cell post integration run time. But a run time connecting to multiple sources has limitations and requires multiple shares connecting to your data if you are ingesting it from on-premises. 

Technical support is okay. Support is contracted or partnered with various companies but is fine as a first level. 

Most of the time, technical support has to connect with product engineers who troubleshoot issues. 

The setup is not very complex but requires intake, setting up integration services, and connecting to databases like Oracle before you push it to service.

We implemented the solution in-house. 

The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper. 

In the cloud, everything is service based and expensive. Users should be knowledgeable enough to maximize the solution. 

For example, it makes no sense to run integration services all day if you are not ingesting data because you pay for that usage. It is important to understand how the product works to manage it accordingly and keep costs down. 

I rate the solution a six out of ten.