Azure Data Factory Reviews

We primarily use the solution for data warehousing. I'm integrating data from multiple sources.

I have a team helping me out from India. It helps manage the data and collect everything from sources.

The product has a lot of features.

It makes it easy to collect data from different sources.

The solution offers good integration with pretty much any system.

I like that it is cloud-based. 

Sometimes when I run some jobs, I have issues with the log flow. I want to see where the data goes. I want to see the data stream.

I'd like more integrations with other APIs. Sometimes I need to do some coding, and I'd like to avoid that. I'd like no-code integrations. 

I've been using the solution for four or five years. 

I'm using the solution as an end-user.

We are using the latest version of the solution. 

I'd rate the solution seven 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.

My clients use Data Factory to exchange information between the on-premises environment and the cloud. Data Factory moves the data, and we use other solutions like Databricks to transform and clean up the data. My teams typically consist of three or four data engineers.

I like how you can create your own pipeline in your space and reuse those creations. You can collaborate with other people who want to use your code.

DataStage is easier to learn than Data Factory because it's more visual. Data Factory has some drag-and-drop options, but it's not as intuitive as DataStage. It would be better if they added more drag-and-drop features. You can start using DataStage without knowing the code. You don't need to learn how the code works before using the solution.

I think the communication about the ADA's would be interesting to see in the platform. How to interact with those kind of information and use it on your pipelines.

I have used Data Factory for eight months.

I have never experienced downtime with Data Factory. 

It isn't that expensive to scale Data Factory up. My client can ask for more resources on the tool, and paying more is never an issue. 

I rate Azure support seven or eight out of 10. There is room for improvement. Sometimes, you don't know where the errors originate. You have to send a ticket to Azure, and they take two or three days to respond. The issue may resolve itself by then. The problem is fixed, but you don't know how to prevent it or what to do if it happens in the future. 

The data transfer has stopped a few times for unknown reasons. We don't know if the resources are insufficient or if there is a problem with the platform. By the time we hear back from Microsoft, the issue has been resolved.

Positive

Data Factory is effortless to set up.

I rate Azure Data Factory nine out of 10. When implementing Data Factory, you should document where you are building so you can pass that information. Sometimes you build something for a specific purpose, but you can use that information for other solutions. If you have a community where you are building things, you can reuse them on the platform, so don't need to build everything from scratch.

We created data ingestion solutions. We have built interpreters, and we have data factories that pull data from our clients. They submit data via Excel spreadsheets, and we process them into a common homogeneous format.

It has helped with some automation. Instead of individual people reviewing these files, we were able to automate the ingestion process, which saved a bunch of time. It saved hours of repeated manual work.

The data mapping and the ability to systematically derive data are nice features. It worked really well for the solution we had. It is visual, and it did the transformation as we wanted.

I couldn't quite grasp it at first because it has a Microsoft footprint on it. Some of the nomenclature around sync and other things is based on how SSRS or SSIS works, which works fine if you know these products. I didn't know them. So, some of the language and some of the settings were obtuse for me to use. It could be a little difficult if you're coming from the Java or AWS platform, but if you are coming from a Microsoft background, it would be very familiar.

For some of the data, there were some issues with data mapping. Some of the error messages were a little bit foggy. There could be more of a quick start guide or some inline examples. The documentation could be better.

There were some latency and performance issues. The processing time took slightly longer than I was hoping for. I wasn't sure if that was a licensing issue or construction of how we did the product. It wasn't super clear to me why and how those occurred. There was think time between steps. I am not sure if they can reduce the latency there. 

I have been using this solution for a year and a half.

It is very stable.

It is very scalable. It is a cloud product. It is being used by business analysts, business managers, and Azure cloud architects. We have just one developer/integrator for deployment and maintenance purposes.

We have plans to increase its usage. We'll be rolling it out for other clients.

Microsoft has these things well-documented. There were videos. I was able to find answers when I needed them. To the uninitiated, it was a little difficult, but we got there.

It was of medium complexity. Because it goes to the cloud, the duration was short. The deployment was minutes and hours.

We are a consultant and integrator. You can use our company for its implementation.

I would rate this solution a nine out of ten.

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.

We use Azure Data Factory to set up a data lake information repository.

Most of our customers are Microsoft shops and prefer Azure Data Factory because they have good licensing options and a trust factor with Microsoft. Our core customers are traditional Microsoft shops who prefer to expand on Microsoft on-cloud.

Azure Data Factory's pricing in terms of utilization could be improved. Our customers complain that the solution's bills keep growing.

I have worked with Azure Data Factory for more than five years as a consultant.

Azure Data Factory is a pretty stable solution. I rate Azure Data Factory a nine out of ten for stability.

Azure Data Factory is a scalable solution. We have around 15 to 20 customers, of which about 8 to 10 use Azure Data Factory.

Azure Data Factory's initial setup needs some amount of professional training and qualification.

It seems very low initially, but as the data grows, the solution’s bills grow exponentially.

Azure Data Factory is deployed on-cloud for our clients.

Azure Data Factory is a pretty solid solution with all the factors and integration built in. It's as good as any product.

I recommend users compare with Snowflake before choosing Azure Data Factory. We've had customers who prefer Snowflake just for its ease of use. Since we're not a Microsoft official reseller, I give options to customers and then let them pick and choose some from Azure Data Factory, Snowflake, or anything on Amazon.

Overall, I rate Azure Data Factory an eight out of ten.

As a management consultancy company, we help our clients deploy Azure Data Factory or any other cloud-based solution depending on data integration needs. Regarding how we use Azure Data Factory within our company, we are on the Microsoft Stack, so we use the solution primarily for data warehousing and integration.

The feature I found most helpful in Azure Data Factory is the pipeline feature, including being able to connect to different sources.

I also found running Python codes whenever you need to valuable in Azure Data Factory, especially for certain features of the solution, such as data integrations, aggregations, and manipulations.

Azure Data Factory also has built-in security, which is another valuable feature.

I also like that you get access to the whole Azure suite through Azure Data Factory, so the overall architecture design, defining security and access, role-based access management, etc. It's helpful to have the whole suite when designing applications.

Areas for improvement in Azure Data Factory include connectivity and integration.

When you use integration runtime, whenever there's a failure, the backup process in Azure Data Factory takes time, so this is another area for improvement.

Database support in the solution also has room for improvement because Azure Data Factory only currently supports MS SQL and Postgres. I want to see it supporting other databases.

If you want to connect the solution from on-premises to the cloud, you will have to go with a VPN or a pretty expensive route connection. A VPN connection might not work most of the time because you have to download a client and install it, so an interim solution for secure access from on-premise locations to the cloud is what I want to see in Azure Data Factory.

I've been using Azure Data Factory for about a year now.

Azure Data Factory is very stable, so it's a four out of five for me. In some instances, the solution failed, but I wouldn't wholly blame Azure Data Factory because my company connected to some on-premise databases in some cases. Sometimes, you'll get errors from self-hosted integration, faulty connections, or the on-premise server is down, so my rating for stability is a four.

Scalability-wise, Azure Data Factory is a four out of five because Microsoft is still developing certain tiers, which means you can't upgrade an older skill or tier. In contrast, the more modern, newer tiers could be upgraded easily. Rarely will you get stuck in one platform where you have completely destroyed that container and then fit a new container. Most of the time, Azure Data Factory is pretty easy to scale.

We haven't used Microsoft support directly because whenever we have issues with Azure Data Factory, we can find resolutions through their online documentation.

We're using both Azure Data Factory and SSIS.

We had several in-house solutions, but we moved to Azure Data Factory because it was straightforward. From a deployment standpoint, the solution comes with different services, so we didn't have to worry about separate hardware or infrastructure for networking, security, etc.

The initial setup for Azure Data Factory was easy, so I'd rate the setup a four out of five.

The implementation strategy was looking into what my organization needed overall, then planning and direct deployment. My company first did a test, a dummy version, then a UAT with stakeholders before going into production.

It took about two months to complete the deployment for Azure Data Factory.

An in-house team, the digital data engineering team, deployed Azure Data Factory.

We're still computing the ROI from Azure Data Factory. It's too early to comment on that.

My company is on a monthly subscription for Azure Data Factory, but it's more of a pay-as-you-go model where your monthly invoice depends on how many resources you use.

On a scale of one to five, pricing for Azure Data Factory is a four.

It's just the usage fees my company pays monthly. No support fees because my company didn't need support from Microsoft.

If you're not using core Microsoft products, the cost could be slightly higher, for example, when using a Postgres database versus an MS SQL database.

My company uses Azure Data Factory, SSIS, and for a few other instances, Salesforce.

My company currently has about fifty Azure Data Factory users, but not directly exposed to the solution compared to the developers; for example, members of corporate management and other teams apart from the development team are exposed to Azure Data Factory.

Shortly, there could be about two hundred users of Azure Data Factory within the company.

The developer team working directly on Azure Data Factory comprises ten individuals.

For the maintenance of the solution, my company has two to three staff, but it could reach up to eight or ten for the entire product. It's a mix of engineers and business analysts who handle Azure Data Factory maintenance.

I'd rate Azure Data Factory as eight out of ten.

My company is an end user of Azure.

We primarily use the solution in a data engineering context for bringing data from source to sink.

The solution is very comfortable to use. I'm happy with the user interface and dashboards. I'm pretty happy with everything about the solution. 

We haven't had any issues connecting it to other products.

It's a stable product. 

I have not found any real shortcomings within the product.

I've been using the solution for the past year. 

The product has been very stable and reliable. I'd rate the stability nine out of ten. There are no bugs or glitches. It doesn't crash or freeze. 

There is a team of 30 people working on the solution. 

I've connected with technical support a few times. 

They sent a support engineer or a field engineer to us, and he helped us out. 

Positive

I'm not sure about the exact cost of the solution. 

I'm a customer and end-user.

Our company chose to use this solution based on the fact that it is a Microsoft product. We're using a lot of solutions, including Outlook and Teams. We also use Power BI. We try to use Microsoft so that everything is under one umbrella. That way, there is no difficulty with connecting anything. 

It's a good solution to use. There are lots of videos available on YouTube, and it is very easy to learn. It's very easy to perform things on it as well, which is one thing that a product like ThoughtSpot lacks. There is no training needed like Power BI. 

I'd rate the solution nine out of ten.