We have not used another solution to this magnitude for real development and production. We work a little bit on Google Cloud. 

I primarily work with Informatica, Azure, and Snowflake for data pipeline tasks.

We've used Talend, SAP BODS, and AWS Glue. Oracle Data Integrator (ODI).

Now, we primarily use products within the Azure ecosystem, like Snowflake and Informatica Cloud Services (ICS). Specifically, we use Informatica Data Management Cloud (IDMC).

Our goal is to migrate from on-premises to IDMC, and we've been using ADF for the past two years to convert SSIS scripts.

Previously I used AWS Glue and SSIS.

Our organization did not switch from a previous solution to Azure Data Factory; rather, we implemented Azure Data Factory as a new solution to enable cloud data processing capabilities.

We previously used different solutions.

I use several similar products by different vendors including Talend, Informatica, and Microsoft SSIS. The biggest advantage that Azure has is deployment. However, in others, it is possible to specify custom data delimiters.

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

We used SSIS, but its on-premise version is slower than Azure Data Factory, and Azure Data Factory, infrastructure-wise, is better, so we went with Azure Data Factory.

We have been and still rely on SSIS for our ETL. ADF seems to do ELT well but I would not consider it for use in ETL at this time.  Its mapping data flows are too slow (which is a large understatement) to be of practical use to us. Also, the ARM template situation is impractical for hundreds of pipelines like we would have if we converted all our SSIS packages into pipelines as a single ADF couldn't take on all our pipelines. 

We had only our own tools, and we switched because you get to leverage all of the work done in a SaaS or platform as a service, or however they classify it. As a result, you get more functionality, faster, for less money.

Before working with Azure, I worked with Python. In the culture I was working in, there was no integration. We were using Pure Python scripting and Python data manipulation tools. For example, we used Python's pandas library, which we coded to transform and orchestrate the data, which is necessary for the endpoint. It was not at all a visual tool. It took more time than Denodo. 

I have experience with Informatica and I find it easier to use. For example, there are a lot of connectors that are directly available. Also, Informatica is able to take incremental copies, but with Azure, you have to write code to do that.

I have also worked with Matillion and Fivetran, and I feel that there are a lot of things that Azure can learn from these products. For example, with Fivetran there are very good connectors for copying data between other solutions. This is unlike Azure, where a lot of the time, I have to build my own logic.

In the past, we used Oracle Data Integrator.

Previously, I was using Informatica. My company wanted to shift to a solution that could be deployed on the cloud, so we chose Azure Data Factory.

My company used Informatica PowerCenter in the past but I was not involved in that.

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.

We previously use Attunity and we switch to Azure Data Factory mainly because of cost reasons and integration.

The biggest difference between Azure Data Factory and Attunity is Attunitys has the ability to perform change data capture. Whereas Azure Data Factory is more centered around batch or bulk loads.

I previously worked with Azure SQL database.

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

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.

We previously used SSIS because of Microsoft.

I've previously used Microsoft SSIS, which is the last incarnation of Azure Data Factory. I've also used IBM Data Manager and Teradata's Management Loads, so I have quite a bit of experience in this area. One of the major differences is that Azure Data Factory is a SaaS service. The other solutions were in-house and you managed your own infrastructure to run them. What I get from Data Factory is a lot better than what I got for the other products. Azure Data Factory is great because it's evolving day-to-day.

We managed the same actions with a combination of tools. We used SFTP servers to move data from one place to another. We used scripts for loading and some other stored procedures or processes for data transformation within a database. It took two or three pieces of technology or systems to manage the same types of operation. Data Factory lets us consolidate those steps into a single pipeline. 

I have also worked with Talend. I didn't switch products, but rather companies.

Talend is a more robust enterprise-wide solution that can handle everything from start to finish, whereas Azure Data Factory is more of an ingestion tool.

The customer used a lot of homebrew stuff. They were doing a lot of internal stuff and some Oracle stuff. They were doing things, and they made a workaround and said, "Okay, we'll bring it into Oracle Database, and then we'll do all these things to it." We're like, "Okay, that works, but then you're taking it out of that database and putting it over into the data lake. I don't understand why are you doing that?" That's what they were doing.

I have only used Data Factory for the cloud. For on-prem we have used SSIS.

I previously worked with Talend, Matillion, and Fivetran.

We started with iConduct and as we migrated to the Azure environment, we began the process of switching to the Microsoft Azure Data Factory.

When I first started in this field, everything was basically Hadoop on-premise and Hadoop infrastructure. With the increase in cloud integrations, things have changed. Once the big data services got introduced, we were probably one of the few companies in North America that were actually into analytics and big data and we were the first to implement related Microsoft products in Canada.

We had been using various ETL tools during the years before moving to the cloud. We picked Azure Data Factory because we were moving towards the Azure cloud.

We did not use another solution prior to this one.

I'm mostly working with cloud technology, and we use other vendors. On-premises, there is another team in my company that's working with Oracle tools. I'm focusing only on Azure technology, which means that I personally don't have hands-on experience with different tools.

We use Microsoft products such as Azure Databricks.

Azure Data Factory is the only solution that I have used since I started with this company.

We have used other ETL solutions in the past, and Azure Data Factory is the best one. Compared to SSIS, for example, ADF is easier to use and the performance is better.

Our clients are migrating from on-premises SSIS solutions to the cloud because they want to take advantage of the latest technologies.

We previously used the Oracle Warehouse Builder. The reasons we stopped using it were that Warehouse Builder is at the end of its life-cycle and its slow performance.

We used SSIS. We're still using SSIS. SSIS is an old product. The development of SSIS has more or less stopped and the development is now focused on cloud services — it's the future. Azure Data Factory Is great because it's a cloud service; you do not have to take care of the installation and configuration yourself. The cost buildup is also quite different. I am not sure that's a huge financial advantage yet, but we do believe that it will be in the future.