Azure Data Factory Reviews

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.

My primary use case of Azure Data Factory is supporting the data migration for advanced analytics projects. 

One of the most valuable features of Azure Data Factory is the drag-and-drop interface. This helps with workflow management because we can just drag any tables or data sources we need. Because of how easy it is to drag and drop, we can deliver things very quickly. It's more customizable through visual effect. 

Data Factory could be improved by eliminating the need for a physical data area. We have to extract data using Data Factory, then create a staging database for it with Azure SQL, which is very, very expensive. Another improvement would be lowering the licensing cost. 

I have been using this solution for the past year. 

This solution is stable. We are using an Azure subscription, so there is no maintenance or direct updates, it's just always the latest version.

This solution is automatically scalable, since it's in the cloud. At my company, there were more than one thousand people using this solution because we were a big, media-based company. If there are many user requests in the front end application and the system is not responding much or has slow performance, the system will automatically scale up the performance hardware requirements. 

I have contacted technical support. I have never faced an issue like that with Denodo. Fortunately, we got some kind of a tutorial PDF, which helps us to deploy everything quickly. 

Before working with Azure, I worked with Python. In the culture I was working in, there was no integration. We were using Pure Python scripting and Python data manipulation tools. For example, we used Python's pandas library, which we coded to transform and orchestrate the data, which is necessary for the endpoint. It was not at all a visual tool. It took more time than Denodo. 

There is no installation because it's on the cloud. You just log on to the cloud with your subscription credentials, then you can use Data Factory directly. 

I implemented through an in-house team. 

Data Factory is very expensive. We are using an Azure subscription, so Data Factory has no direct updates, it's just always the latest version. Compared to Denodo, Azure is very costly. Azure Framework has multiple services, not only Data Factory. So in the cloud-based solution, if you're selecting a particular service, like Data Factory, you need to pay for each request.

I also use Denodo. Data Factory is like a transformation layer, but we need an additional staging database or a data storage facility, which is very expensive compared to implementing Denodo. So we extracted the data using Data Factory, then created a staging database with Azure SQL, which cost a huge amount since it's a physical data area. In Denodo, we just implement a layer, which is all handled in Denodo, and not a physical storage mechanism. I prefer customizable data solutions because they improve performance, creativity, and are helpful for front end people.

In comparison to Data Factory's drag-and-drop interface, Denodo developers need to create all the unified views by coding, so we have to create SQL queries to execute. With Data Factory, you can quickly drag and drop data or tables, but in Denodo, it takes more time because you need to code and test and all that.

I rate Data Factory an eight out of ten, mainly because you need a staging database. I recommend Azure to others, but it depends on architecture. In Data Factory, there is no virtualization environment, no layer of virtualization to help integration and doing caching mechanisms. Though Data Factory is there, Denodo is going further. 

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'm not sure how much information I can provide regarding to some kind of security of my company, but I can tell you that we were migrating integrations from from platform to the other, and the other platform was error data factory.

For me, it was that there are dedicated connectors for different targets or sources, different data sources. For example, there is direct connector to Salesforce, Oracle Service Cloud, etcetera, and that was really helpful.

It's a good idea to take a Microsoft course. Because they are really helpful when you start from your journey with Data Factory.

I have been using Azure Data Factory for one year. 

The solution doesn't have stability issues. 

It is easy to scale. 

The initial setup is straightforward. Deployment is automatic and takes few minutes. 

Overall, I would rate it an 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 to extract, transform, and load the data into the database.

Feature-wise, one of the most valuable ones is the data flows introduced recently in the solution.

Currently, our company requires a monitoring tool, and that isn't available in Azure Data Factory. Although the platform displays which pipelines are running, it doesn't offer a monitoring tool that allows for the sequential execution of pipelines and the ability to visualize end-to-end data flow. As such, this feature is currently missing from the platform.

I have been using Azure Data Factory for more than six years. Also, I am an end-user of the solution, and I initially used to work on Azure Data Factory V1. Now, I have switched to Azure Data Factory V2.

It is a stable solution. Stability-wise, I rate the solution an eight or nine out of ten.

Scalability-wise, I rate the solution a seven or eight out of ten. So, scalability can be improved. Also, there are around 150 people in my company using the solution. Moreover, we use the solution daily in our company.

I rate the technical support between eight to nine out of ten.

Positive

Previously, I was using Informatica. My company wanted to shift to a solution that could be deployed on the cloud, so we chose Azure Data Factory.

The solution's initial setup process was easy. On a scale where one is difficult and ten is easy, I rate the initial setup process an eight out of ten. The solution is deployed on the cloud.

Since multiple projects are going on in my organization, there is no uniformity in the time taken to deploy the solution in our company. However, I can say that it only takes a few days to carry out the deployment process.

Our organization has multiple project teams, so each team carries out its deployment process.

To give an average, I would consider that if there are fifty ongoing projects in our company, and if we consider a person from each project, fifty people are needed for the deployment and maintenance process.

The solution's implementation process was done with our in-house team's help.

I cannot comment on the pricing parts since our company's admin team handles it.

Those who want to move to a cloud platform can choose Azure Data Factory since it is the best tool. Since certain improvements are required in the solution, I rate the overall solution an eight out of ten.

I primarily use Data Factory to ingest data. For example, if we need to pull data into our data warehouse from somewhere like Azure Event Hub or salesforce.com.

We have telemetry that streams into an Azure Event Hub, and Data Factory allowed us to move that data from the Event Hub into our data lake and reduce the cost of that compared to the other tooling we were using.

Data Factory's best features are simplicity and flexibility. It's been very easy to set up connections to different types of data sources to pull data into our warehouse.

Data Factory would be improved if it were a little more configuration-oriented and not so code-oriented and if it had more automated features.

I've been using Data Factory for about three years.

I would rate Data Factory's stability eight out of ten.

I would rate Data Factory's scalability eight out of ten.

The initial setup was straightforward, and only one person was required for deployment.

I would rate Data Factory's pricing nine out of ten.

I think Data Factory is a good fit when you need a light, inexpensive way to ingest data. I would rate it eight out of ten.

Our primary use case is for traditional ETL; moving data from the web and data sources to a data warehouse. I've also used Data Factory for batch processing and recently for streaming data sets. We have a partnership with Microsoft and I am a cloud solution architect.

There are a lot of innovative features that Microsoft releases regularly. The UI is easy to navigate and finding information on new features has proven to be quite easy. I like that I can retrieve VTL code pretty quickly without knowing in-depth coding languages like Python. Microsoft has very good support teams that I've dealt with and they are very helpful in resolving problems.

Improvement could be made around streaming data because I feel that the Data Factory product is mainly geared for batch processing and doesn't yet have in-built streaming data processing. Perhaps it's on the way and they are making some changes to help facilitate that. If they were to include better monitoring that would be useful. I'd like to see improved notifications of what the actual errors are.

I've been using this solution for four years. 

The solution is stable. 

The way we've used the product is around streaming data, and that doesn't work well with Data Factory. As loads increase, some of the underlying infrastructure that gets used to process data seems to slow down. This is basically a development product, so in terms of scalability it doesn't have a wide user base, it's only meant for developers and analysts. The number of users will vary from anywhere between one to five people. 

The customer service is excellent. 

I've previously used Microsoft SSIS, which is the last incarnation of Azure Data Factory. I've also used IBM Data Manager and Teradata's Management Loads, so I have quite a bit of experience in this area. One of the major differences is that Azure Data Factory is a SaaS service. The other solutions were in-house and you managed your own infrastructure to run them. What I get from Data Factory is a lot better than what I got for the other products. Azure Data Factory is great because it's evolving day-to-day.

The initial setup is relatively straightforward. That's assuming that when you're creating the Data Factory, you have some knowledge about how to create it. We deployed in-house and it took about ten minutes for the initial creation process. The provisioning itself is not something you have control over because it's a self-service that does what it needs and happens in the background. The infrastructure doesn't require any maintenance, it's all managed on the backend. There is maintenance in terms of your codes, connections, and the like, but that is separate from the infrastructure maintenance.

The Data Factory itself will not give you a return on investment, it's the entire solution that brings a return, I'd say at least 20% to 30% of return for investment over a five-year period. 

Azure Data factory is a pay-as-you-go service so cost depends on the number of connections, how many times each activity in that node is run, and how much data gets moved. There are a number of factors that define the price, but it pairs with your service. You're charged for the amount of data that's moved, but there are no charges for the features you use.

I would definitely recommend this solution. If you decide to implement Data Factory, I suggest reaching out to qualified professionals because there are a lot of moving parts. That said, if you have internally qualified staff, deployment shouldn't be a problem. Apart from a few minor issues, it's pretty reliable with good support and a whole bunch of resources available on the web.

I rate this solution a solid nine out of 10.