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.
Works with 5,001-10,000 employees
Easy to set up, and reasonably priced, but the user experience could be improved
Pros and Cons
- "Microsoft supported us when we planned to provision Azure Data Factory over a private link. As a result, we received excellent support from Microsoft."
- "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."
What is most valuable?
What needs improvement?
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.
For how long have I used the solution?
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.
What do I think about the stability of the solution?
Azure Data Factory is a stable solution.
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What do I think about the scalability of the 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.
How are customer service and support?
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.
Which solution did I use previously and why did I switch?
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.
How was the initial setup?
The initial setup is easy.
It took three to four weeks to get up to speed and get comfortable using it.
What's my experience with pricing, setup cost, and licensing?
Pricing appears to be reasonable in my opinion.
What other advice do I have?
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.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Head of Product at Tata Consultancy
Stable storage solution used to extract and store data to improve our BI functionality
Pros and Cons
- "When it comes to our business requirements, this solution has worked well for us. However, we have not stretched it to the limit."
- "The one element of the solution that we have used and could be improved is the user interface."
What is our primary use case?
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.
What needs improvement?
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.
For how long have I used the solution?
I have used this solution for four years.
What do I think about the stability of the solution?
This is a stable solution.
How are customer service and support?
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.
What other advice do I have?
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.
Disclosure: My company has a business relationship with this vendor other than being a customer: Partner
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Independent consultant at a hospitality company with 1-10 employees
Has a user-friendly interface and robust data-monitoring capabilities
Pros and Cons
- "The platform excels in data transformation with its user-friendly interface and robust monitoring capabilities, making ETL processes seamless."
- "The product integration with advanced coding options could cater to users needing more customization."
What is our primary use case?
The platform simplifies data access and visualization with minimal coding, catering to various data management needs across different client projects.
How has it helped my organization?
The product centralizes data workflows, enhancing data integration efficiency and visualization for comprehensive analysis.
What is most valuable?
The platform excels in data transformation with its user-friendly interface and robust monitoring capabilities, making ETL processes seamless.
What needs improvement?
The product integration with advanced coding options could cater to users needing more customization.
For how long have I used the solution?
I've been using Azure Data Factory for almost a year now.
What do I think about the stability of the solution?
The product is stable.
What do I think about the scalability of the solution?
The product is scalable.
How was the initial setup?
The initial setup process is straightforward.
What's my experience with pricing, setup cost, and licensing?
Azure products generally offer competitive pricing, suitable for diverse budget considerations.
What other advice do I have?
Data Factory integrates seamlessly within the Azure ecosystem, offering robust data management capabilities across large-scale projects.
I rate it a nine out of ten.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Last updated: Jul 30, 2024
Flag as inappropriatePrincipal at a tech services company with 51-200 employees
Good product integrations but transient issues sometimes cause pipeline failures
Pros and Cons
- "It is beneficial that the solution is written with Spark as the back end."
- "There are limitations when processing more than one GD file."
What is our primary use case?
Our company uses the solution for data ingestion.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been using the solution for two years.
What do I think about the stability of the solution?
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.
What do I think about the scalability of the solution?
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.
How are customer service and support?
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.
How was the initial setup?
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.
What about the implementation team?
We implemented the solution in-house.
What's my experience with pricing, setup cost, and licensing?
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.
What other advice do I have?
I rate the solution a six out of ten.
Which deployment model are you using for this solution?
Private Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Microsoft Azure
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Senior Director/ Advisory Architect at a tech vendor with 10,001+ employees
Mature and highly configurable solution
Pros and Cons
- "Data Factory's best features include its data source connections, GUI for building data pipelines, and target loading within Azure."
- "Data Factory's performance during heavy data processing isn't great."
What is most valuable?
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.
What needs improvement?
Data Factory's performance during heavy data processing isn't great.
What do I think about the stability of the solution?
Data Factory is stable - I have customers running thousands of jobs a day without problems.
What do I think about the scalability of the solution?
Data Factory is scalable.
How are customer service and support?
Microsoft's technical support is pretty good.
How was the initial setup?
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.
What other advice do I have?
Data Factory is a good, mature solution, and I would rate it as eight out of ten.
Which deployment model are you using for this solution?
Private Cloud
Disclosure: My company has a business relationship with this vendor other than being a customer: Partner
.NET Architect at a computer software company with 10,001+ employees
A cloud-based data integration service that's easy to understand and use
Pros and Cons
- "I think it makes it very easy to understand what data flow is and so on. You can leverage the user interface to do the different data flows, and it's great. I like it a lot."
- "It would be better if it had machine learning capabilities."
What is our primary use case?
I use Azure Data Factory in my company because we are implementing a lot of different projects for a big company based in the USA. We're getting certain information from different sources—for example, some files in the Azure Blob Storage. We're migrating that information to other databases. We are validating and transforming the data. After that, we put that data in some databases in Azure Synapse and SQL databases.
What is most valuable?
I think it makes it very easy to understand what data flow is and so on. You can leverage the user interface to do the different data flows, and it's great. I like it a lot.
What needs improvement?
It would be better if it had machine learning capabilities. For example, at the moment, we're working with Databricks and Azure Data Factory. But Databricks is very complex to do the different data flows. It could be great to have more functionalities to do that in Azure Data Factory.
For how long have I used the solution?
I have been using Azure Data Factory for about one year.
What do I think about the stability of the solution?
Azure Data Factory is a stable solution.
What do I think about the scalability of the solution?
It's scalable. We're doing a lot of different integrations with a lot of data, and scalability is great.
How was the initial setup?
The initial setup is straightforward. I think that it's so easy to start a project using that technology.
What about the implementation team?
We have a team that's in charge of doing the deployments in Azure in different environments.
What other advice do I have?
I would tell potential users that there are many technologies to do this. For example, if you like to manage big data and do something with it, it would be better to use Databricks.
On a scale from one to ten, I would give Azure Data Factory a nine.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Microsoft Azure
Disclosure: My company has a business relationship with this vendor other than being a customer: Partner
General Manager Data & Analytics at a tech services company with 1,001-5,000 employees
Great data pipeline and the orchestration functionality with a good user interface
Pros and Cons
- "The initial setup is very quick and easy."
- "Integration of data lineage would be a nice feature in terms of DevOps integration. It would make implementation for a company much easier. I'm not sure if that's already available or not. However, that would be a great feature to add if it isn't already there."
What is our primary use case?
The solution is primarily used for data integration. We are using it for the data pipelines to get data out of the legacy systems and provide it to the Azure SQL Database. We are using the SQL data source providers mainly.
What is most valuable?
The data pipeline and the orchestration functionality are the most valuable aspects of the solution.
The interface is very good. It seeks to be very responsive and intuitive.
The initial setup is very quick and easy.
What needs improvement?
I'm more of a general manager. I don't have any insights in terms of missing features or items of that nature.
Integration of data lineage would be a nice feature in terms of DevOps integration. It would make implementation for a company much easier. I'm not sure if that's already available or not. However, that would be a great feature to add if it isn't already there.
For how long have I used the solution?
We've used the solution for the last 12 months or so.
What do I think about the stability of the solution?
From what I have witnessed, the solution is quite stable. It doesn't crash or freeze. There are no bugs or glitches. It's reliable.
What do I think about the scalability of the solution?
We work with medium to enterprise-level organizations. Customers have anywhere from 300 employees up to 160,000 employees.
How are customer service and technical support?
Microsoft offers a great community. There's a lot of support available. We're quite satisfied with the level of assistance on offer.
How was the initial setup?
Since the solution is a service, it's basically just a click and run setup. It's very simple. There's very little implementation necessary. A company should be able to easily arrange it. The deployment doesn't take very long at all.
What about the implementation team?
We do provide the implementation for our clients. We're able to provide templates as well. We have predefined implementation space in Data Factory and provide it to the customer.
Which other solutions did I evaluate?
While clients might individually evaluate other options, however, we're not aware of that information. I can't say what other solution clients might consider before ultimately choosing Microsoft. I would say that it is likely Talend and maybe SQL Server Integration Services.
What other advice do I have?
We are like an integrator. We are a data warehouse NPI consulting company and we use Data Factory to pull data from different legacy systems and do all these transformations that are necessary in order to provide analytical models.
In our normal scenario is that we are providing Azure SQL Databases together with Azure Data Factory and Power BI. 80% of our customers have recognized such a scenario.
On a scale from one to ten, I'd rate the solution at an eight. We've been largely happy with the capabilities of the product.
Disclosure: My company has a business relationship with this vendor other than being a customer: Implementator
Azure Technical Architect at a computer software company with 10,001+ employees
Has the ability to copy data to any environment
Pros and Cons
- "From my experience so far, the best feature is the ability to copy data to any environment. We have 100 connects and we can connect them to the system and copy the data from its respective system to any environment. That is the best feature."
- "The user interface could use improvement. It's not a major issue but it's something that can be improved."
What is our primary use case?
It's an integration platform, we migrate data across hybrid environments. We have data in our cloud environment or on-prem system so we use it for when we want to integrate data across different environments. It was a problem for us to get data from different hybrid environments.
What is most valuable?
From my experience so far, the best feature is the ability to copy data to any environment. We have 100 connectors and we can connect them to the system and copy the data from its respective system to any environment. That is the best feature.
What needs improvement?
The user interface could use improvement. It's not a major issue but it's something that can be improved.
It has the ability to create separate folders to organize objects, Data Factory objects. But any time that we created a folder we were not able to create objects. We had to drag and drop into the folder. There were no default options. It was manual work. We offered their team our feedback and they accepted my request.
For how long have I used the solution?
I have been using Azure Data Factory for around one year.
What do I think about the stability of the solution?
Based on my experience with other products on the market, the stability is good.
What do I think about the scalability of the solution?
I haven't had much experience with scalability. I know we do have scalability options though. It's used daily.
There are around 1,000 plus users using this solution in my company.
It requires two people for maintenance. The administrators are the ones who maintain it and give access to the engineers. They regulate who has privileges.
How are customer service and technical support?
We have needed to contact their technical support. If we can't find the answers ourselves on the blogs, we contact them with our questions. We get most of the answers we need from the blogs but if not then we can directly speak to the Microsoft team from the Data Factory interface itself, it's really helpful.
Which solution did I use previously and why did I switch?
I have only used Data Factory for the cloud. For on-prem we have used SSIS.
How was the initial setup?
The initial setup was a bit complex but once you understand its setup, it's less complex. There are certain processes that need to be followed. Once you understand the process, it becomes easier to implement.
The implementation took a little less than one day. The planning for the deployment takes around one or two days.
What about the implementation team?
We had a discussion with the Microsoft team about the data. We discussed how we were going to implement. Based on the discussion we were able to deploy. A Microsoft partner helped us with some parts.
Which other solutions did I evaluate?
We also evaluated AWS.
What other advice do I have?
The advice that I would give to someone considering this solution is to have some background in data warehousing and ETL concepts. Have the background about data warehousing and ETL that extract, transform, and load. If you have the background you need, you will be successful. If not, then my advice would be to learn a little more about it before using Data Factory.
I would rate Data Factory as an eight out of ten.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Disclosure: My company has a business relationship with this vendor other than being a customer: Partner
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