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.

The primary use case is to connect to various different data sets and do an EAT into our data warehouse.

We have been using drivers to connect to various data sets and consume data. The solution gives everything under one roof, which is an important feature.

We require Azure Data Factory to be able to connect to Google Analytics.

I have been using the solution for two years.

The solution is stable.

The solution is scalable.

We had a few technical calls with the Microsoft technical support team for some issues that we were facing, which they helped us resolve.

Positive

The initial setup is straightforward and the team is able to deploy between six and seven days.

The implementation was completed in-house.

The cost is based on the amount of data sets that we are ingesting. The more data we ingest the more we pay.

I give the solution a nine out of ten. We have been happy with all the customer implementations, and the customers are satisfied with the ADF pipelines. We are also currently examining the Synapse pipelines, which are likely similar.

We have six developers using the solution in our organization.

People should use the solution for two reasons. Firstly, we can switch off any data pipelines we set up to save costs. Secondly, there are several connectors available in one place, including most standard connectors.

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. 

Positive

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.

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.

Azure Data Factory can be deployed on the cloud and hybrid cloud. There have been very few deployments on private clouds.

The most valuable feature of Azure Data Factory is the core features that help you through the whole Azure pipeline or value chain.

Across the whole field of use, from accepting the ingestion and real-time SaaS ingestion for which we often use other components. These areas have been instrumental across the board.

Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities. This is something that is just not fully covered. Additionally, the integration could improve for other tools, such as Azure Data Catalog.

I have been using Azure Data Factory for approximately four years.

The stability of Azure Data Factory is good.

I rate the scalability of Azure Data Factory a seven out of ten.

Azure Data Factory is scalable. The solution can move up and be aligned to resources or scaled down.

We have a lot of customers using the solution, approximately 100.

The support from Azure Data Factory is very good. There are some improvements needed.

I rate the support from Azure Data Factory a four out of five.

Positive

I have previously used Informatica. When comparing Informatica to Azure Data Factory, Informatica is a bit behind.

The initial setup of Azure Data Factory is not complex if you know what you are doing. If you do not know the technology you will have a problem.

Azure Data Factory gives better value for the price than other solutions such as Informatica.

I recommend this solution to others.

I rate Azure Data Factory an 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.

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.