Azure Data Factory Reviews

We use this solution to quickly instantiate certain components in Azure with the aim of having consistent use of certain components and objects. It provides one point of reference rather than having the need to replicate points of references, and then having to keep them in sync. 

It has helped our company by providing consistency. For example, by making sure that the way certain objects and components are defined is consistent throughout every step. If things change at any point, they should be reflected at all points. It's the consistency that makes implementations less buggy and less complex.

The function is the central point of reference and the most valuable thing about Data Factory.

I'd like to see videos on YouTube or the Microsoft site with more detailed implementations. The solution lacks a decent UI that allows us to see what kinds of requests are for what objects and how the population of objects is being requested and compared. Right now we have to look at logs to get an idea of what types of calls the data factory receives in what sequence, for example. It would be nice to be able to see it graphically because we currently have to interpret the logs and then create a graphical representation to have an idea of what's going on. In general, it could be simplified and made more user-friendly.

I've been using this solution for six months.

This solution is stable. 

There are thousands of users so the solution is scalable. 

The customer service is excellent. 

I previously used a combination of solutions to achieve the same end. Data Factory simplifies things which is why we switched to it. 

The initial setup was complex. The deployment was carried out in-house and we had around 10 people involved in the implementation. 

In terms of time and effort savings, we have a return on our investment. 

It's important to have a good data model before you start using the solution; an idea of the types of data architecture, data objects and components in order to use them. Because of the lack of more user-friendly interfaces, especially for the people debugging the system, I rate this solution eight out of 10.

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 use this solution to ingest data from one of the source systems from SAP. From the SAP HANA view, we push data to our data pond and ingest it into our data warehouse.

Azure Data Factory didn't bring a lot of good when we were also using Alteryx. Alteryx is user-friendly, while Azure Data Factory uses many resources and has issues with parallel workflows. Alteryx helps you diagnose issues quicker than Azure Data Factory because it's on the cloud and has a cold start debugger.

Azure Data Factory has to wake up whenever you are trying to do testing, and it takes about four to five minutes. It's not always online to do a quick test. For example, if we want to test an Excel file to see if the formatting is correct or why the data-flow or pipeline is failing, we need to wait four to five minutes to get the cold start debugger to run. Compared to Alteryx, Azure Data Factory could be better. Nevertheless, we are using it because we have to.

Initially, when we started using it, we didn't like it because it needed to be more mature and had data-flows, so we used the traditional pipeline. After that, Azure Data Factory introduced the concept of data-flows, and it started to become more mature and look more like Alteryx. Azure Data Factory became more user-friendly when data-flows were introduced.

They introduced the concept of Flowlets, but it has bugs. Flowlets are a reusable component that allows you to create data-flows. We can configure a Flowlet as a reusable pipeline and plug it inside different data-flows, so we don't have to rewrite our code or visual transformation.

If we make any changes in our data-flow, it reverts all our changes to the original state of the Flowlet. It does not retain changes, and we must reconfigure the Flowlets repeatedly. We had these issues three months ago so things might have changed. It works fine whenever we plug it in and configure it in our data-flow, but if we make minor changes to it, the Flowlet needs to be reconfigured again and loses the configuration.

We have used this solution for about a month and a half. It is a cloud-based tool, so there are no versions. It is all deployed on Azure Cloud.

Everything is computed inside the SQL server if we're working with pipelines, so we have to be very careful when designing our solution in Azure Data Factory. Alteryx spoiled us because we never cared how it looked in the backend because all the operations were happening on the Alteryx server. But in Azure Data Factory, they run on the capacity of our data warehouse. So Azure Data Factory cannot run your queries, and it directly sends the query to the instance in the SQL server or data warehouse. So we have to be very careful about how we perform certain operations.

We need to have knowledge of SQL and how to optimize our queries. If we are calling a stored procedure, it joins one table in Alteryx. It is pretty easy, and we just put a joint tool. Suppose we want to do it with a stored procedure in the Azure Data Factory. In that case, we have to be very careful about how we write our code. So that is a challenge for our team because we were not looking into how to optimize their SQL queries when fighting queries from Azure Data Factory to the data warehouse.

In addition, the workflows were running very slow, the performance was bad, and some queries were getting timed out because we have a threshold. So we faced many challenges and had to reeducate ourselves on SQL and query optimization.

In regards to scaling, when Azure Data Factory was introduced as your Databricks, it worked similarly to Hadoop or Spark, and it had some Spark clusters in the back end that could scale it as much as it could, and speed up the performance. So it is scalable, especially with Databricks, because a lot of data-related transformations can be performed.

On my team, there are approximately 20 people who work with Azure Data Factory.

We do not have experience with customer service and support.

It does not require any installation and is more like software as a service. You need to create an instance of Azure Data Factory in Azure and configure some of the connections to your databases. You can connect to your block storages and some authentication is necessary for Azure Data Factory.

The setup is straightforward. It doesn't take much time, and it's on cloud. It requires a few clicks, and you can quickly set it up and grant access to the developer. Then the developer can go to the link and start developing within their browser.

We have a team that takes care of the cloud infrastructure, so we raise a ticket and request infrastructure, and they just exceed it based on the naming convention with the project name.

We have an entire team that takes care of the cloud infrastructure. So we raise a ticket when we need infrastructure, which is executed based on the naming convention for the project name.

The nature of our solution is not based on ROI because we are building solutions for other functions within the same organization. In addition, due to the large size of our organization and the services we provide, the ROI is not something we consistently track. It's something discussed with the management, so I can't comment on it.

The cost is based on usage and the computing resources consumed. However, since Azure Data Factory connects with so many different functionalities that Azure provides, such as Azure functions, Logic apps and others in the Azure Data Factory pipelines, additional costs can be acquired by using other tools.

We did not evaluate other options because this solution was aligned with out current work environment. 

I rate the solution a seven out of ten. The solution is good and constantly improving, but the concept of Flowlets can be reconfigured to retain the changes we make. I advise users considering this solution to thoroughly understand what Azure Data Factory is and evaluate what's available in the market. Secondly, to assess the nature of the use cases and the kind of products they will be building before deciding to choose a solution.

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.

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.

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.

I mainly use Data Factory to load data for ETL processes or to Azure Storage and for testing purposes in our business unit.

Data Factory's most valuable feature is Copy Activity.

I've been using Data Factory for around two years.

Data Factory is stable.

We've had no problems with Data Factory's scalability.

Microsoft's technical support is responsive and quick to help.

We used consultants to implement Data Factory.

Data Factory's cost is too high.

I would advise anybody thinking of implementing Data Factory to calculate their costs at the initial stage in order to have some knowledge about future costs for the whole project. I would rate Data Factory as eight out of ten.

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.