The data factory agent is quite good and programming or defining the value of jobs, processes, and activities is easy. We have the agent installed on-premises in order to gather information.  

The cloud includes all kinds of API connections so we can easily gather information from other services. 

The solution seamlessly integrates with the Azure infrastructure. 

The most valuable feature of this solution is the data flow, which is the same SQL server in important service, integration services, which is a very robust and powerful tool to transform data.

The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed.

The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem.

ADF is another ETL tool similar to Informatica that can transform data or copy it from on-prem to the cloud or vice versa. Once we have the data, we can apply various transformations to it and schedule our pipeline according to our business needs. ADF integrates with Databricks. We can call our Databricks notebooks and schedule them via ADF. 

What I like best about Azure Data Factory is that it allows you to create pipelines, specifically ETL pipelines.

I also like that Azure Data Factory has connectors and solves most of my company's problems. I can't recall a case where I couldn't use the solution for solving problems.

I'm also happy about the Azure Data Factory dashboard.

We have found the bulk load feature very valuable.

The most important feature is the Data Catalog. We need to define all the data fields we test. It has technical information in the Data Catalog. The main feature is data ingestion in ADF. We also extended it to PurView because PurView is an extension of the Azure data catalog. It can scan metadata. There is a limitation in ADF when setting up the data catalog.

The most valuable features of the solution are its ease of use and the readily available adapters for connecting with various sources.

It is easy to integrate.

I do not foresee any issues with security.

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.

One advantage of Azure Data Factory is that it's fast, unlike SSIS and other on-premise tools.

It's also very convenient because Azure Data Factory has multiple connectors. It has sixty connectors which you can't find in SSIS. The availability of native connectors allows you to connect to several resources to analyze data streams.

I also like that you can set up your own VM and infrastructure on Azure Data Factory without any help from the IT team because it only requires a single click.

The most valuable aspect has been a large list of no-cost source and target adapters.

It is also providing a PaaS ELT solution that integrates with other Azure resources. 

Its graphical UI is very good and is even now improving significantly with the latest preview feature of displaying inner activities within other activities such as forEach and If conditions.   

Its built-in monitoring and ability to see each activity's JSON inputs/outputs provide an excellent audit trail.

The trigger scheduling options are decently robust.

The fact that it's continually evolving is hopeful that even if some feature is missing today, it may be soon resolved. For example, it lacked support for simple SQL activity until earlier this year, when that was resolved. They have now added a "debug until" option for all activities. The Copy Activity Upsert option did not perform well at all when I first started using the tool but now seems to have acceptable performance.  

The tool is designed to be metadata driven for large numbers of patterned ETL processes, similar to what BIML is commonly used for in SSIS but much simpler to use than BIML. BIML now supports generating ADF code although with ADF's capabilities I'm not sure BIML still holds its same value as it did for SSIS.

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.

The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring.

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. 

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.

The most valuable feature is the ease in which you can create an ETL pipeline.

The GUI is very helpful when it comes to creating pipelines. The user interface is also very fast.

The connection to Snowflake is easy. I can store data and transform it during the ETL process before sending it to Snowflake.

The tool's most valuable features are its connectors. It has many out-of-the-box connectors. We use ADF for ETL processes. Our main use case involves integrating data from various databases, processing it, and loading it into the target database. ADF plays a crucial role in orchestrating these ETL workflows. 

The tool's visual interface is good. The ADS scheduling feature impacts data management by determining when jobs must be run and setting up dependencies. This capability eliminates the need to rely on enterprise data scheduling tools. 

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.

The most valuable aspect is the copy capability.

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

The overall performance is quite good.

As a DevOps engineer, I feel that the CI/CD part and the tool's integration with GitHub are the product's best features. If you compare it with other tools, like Glue, AWS, and other solutions, I feel Azure Data Factory's deployment part is a lot easier to manage. The code promotions and the data pipeline promotions to higher environments are a lot easier with Azure Data Factory.

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. 

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.

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.

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 like that it's a monolithic data platform. This is why we propose these solutions.

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.

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.

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. 

The most valuable features of Azure Data Factory are the flexibility, ability to move data at scale, and the integrations with different Azure components.

The most valuable feature of this solution is that it allows more data between on-premises and cloud solutions. It's also useful for orchestration for complex data flows and allows us to do ETL-based transitions heavily. In addition, it allows us to integrate with other third-party systems and compare features and pricing. Other valuable features include database replication, SQL service products, SLA support, data sharing, vendor lock-in, and support for developer tools.

The overall architecture has been very valuable to us. It has allowed us to scale up pretty rapidly. That's something that has been very good for us. 

The solution can scale very easily.

The stability is very good and has improved very much over time.

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

Data Factory's most valuable feature is Copy Activity.

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.

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.

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.

Azure Data Factory was chosen by the team that I was not on at the time and who decided that this would be the move to the future. So I just went along with it.

In terms of my personal experience, it works fine.

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.

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.

Data Factory itself is great. It's pretty straightforward. You can easily add sources, join and lookup information, etc. The ease of use is pretty good. 

They have a lot of other components like a newer monitor, which helps track and generate alerts for any failed jobs and things of that nature, which is helpful.

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.

It's cloud-based, allowing multiple users to easily access the solution from the office or remote locations. I like that we can set up the security protocols for IP addresses, like allow lists. It's a pretty user-friendly product as well. The interface and build environment where you create pipelines are easy to use. It's straightforward to manage the digital transformation pipelines we build.

The solution is okay.

The best part of this product is the extraction, transformation, and load. In fact, we have found that the three of them work quite well. We are implementing the cloud-based system right now.

We see a lot of improvement with the most recent version of this solution. Some of the new features are very important to us.

It's fairly simple to use. The most valuable feature of this solution would be ease of use.

It is very modular. It works well. We've used Data Factory and then made calls to libraries outside of Data Factory to do things that it wasn't optimized to do, and it worked really well. It is obviously proprietary in regards to Microsoft created it, but it is pretty easy and direct to bring in outside capabilities into Data Factory.

It is very flexible. You can build any features you want.

This solution has provided us with an easier, and more efficient way to carry out data migration tasks.

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.  

The most important feature is that it can help you do the multi-threading concepts. It's in Informatica, but the resourcing is quite robust. You can scale up and scale down as per your needs.

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. 

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.

I like its integration with SQL pools, its ability to work with Databricks, its pipelines, and the serverless architecture are the most effective features.

For developers that are very accustomed to the Microsoft development studio, it's very easy for them to complete end-to-end data integration.

Data Factory's best features are connectivity with different tools and focusing data ingestion using pipeline copy data.

The reason that we implemented this product is for the full integration with the whole Azure environment. It is very important because you don't have to deal with security, identity flow, or audit flow.

The security of the agent that is installed on-premises is very good. This is another important feature for us.

It comes with a lot of connectors out of the box, including support for SQL, Oracle, SAP, and ODBC.

The most valuable part of this product is the ease of use. It is easy to use and rather intuitive. Because it is easy to use, you can do things with it easily. The more things make your work easy, the more valuable they are.  

It has built-in connectors for more than 100 sources and onboarding data from many different sources to the cloud environment.

It has everything we needed.

Being able to post your feedback and queries is a very good feature that is offered by Azure Data Factory.

If you have a very simple pipeline you can use Data Factory for transformations, but it's really for serious analytics. This is an excellent tool for pipeline orchestration; connecting the different components and activities as well as gathering data. It's an orchestration tool, not a transformation tool. The monitoring feature has drastically improved.

The two most valuable features of Azure Data Factory are that it's very scalable and that it's also highly reliable.

The most valuable features are data transformations.

Data Flow and Databricks are going to be extremely valuable services, allowing data solutions to scale as the business grows and new data sources are added.

I like the basic features like the data-based pipelines.

I like Azure Data Factory, it works quite well.

It is always effective. It's extremely consistent.

The most valuable feature is the copy activity. Instead of creating a pipeline and then doing a source to target copy, this feature will create the pipeline directly.

The data copy template is a valuable feature. With the pipeline template, it takes only a few clicks for the on-premises data to come in. 

So far, I'm quite happy with the solution overall. It's got a lot of tools that I use in my work, and these are items I'm already recommending to my clients. I'm quite happy with it.

Azure Data Factory's most valuable features are the packages and the data transformation that it allows us to do, which is more drag and drop, or a visual interface. So, that eases the entire process.

Azure Data Factory setup is quite user-friendly.

I am happy with the interface.

Our clients find that this solution has a very good performance. They like the speed.

It is easy to deploy workflows and schedule jobs. You can just click on the desktop and it works.

The features that I've found most valuable, in order: That it is a complete ETL Solution, the second one is interface, the third one workflow, and the fourth one ease of use.

Its integrability with the rest of the activities on Azure is most valuable.

It is also easier to work with. It has a nice user interface that would seem very familiar to some of the people who are coming from Microsoft SSIS and other similar kinds of activities. It makes it easy for them to pick it up quickly.

The flexibility that Azure Data Factory offers is great.

The user interface is very good. It makes me feel very comfortable when I am using the tool.

I like the feature that allows you to connect to different sources. You can go through forms and click on them and you don't have to provide the scripts. The solution has a good interface and the integration with GitHub is very useful.

There is also a self-service integration that allows you to run the BTS pages on the SQL server from an SIS. Users have an option to configure this self-service integration engine to run items as a part of a pipeline of the Azure data factory.

The support for SAP services and databases, specifically SAP HANA, has been a game-changer for us.