We have multiple banking applications running on SSIS pipelines. We're in the process of upgrading them to a hybrid cloud architecture. For that, we use Azure Data Factory to move data from on-premises to the cloud – mainly for back-end database operations and ETL transformations.

We primarily use it to load data from an on-premises SQL Server to either Blob storage or an Azure SQL data warehouse. For other integrations, especially those outside of Azure, we tend to use Informatica Cloud Services (ICS).

For structured data loading, we use it However, we use Informatica for unstructured or semi-structured data. We also use Snowflake for ETL processes and sometimes for streaming. 

In my opinion, ADF isn't as suitable for streaming – for streaming, Snowflake streamlets or Informatica structured streaming are more reliable. ADF works well for batch processing, though.

When we were integrating the Ports product with our internal data warehouse, we had to update all the reports to our internal data warehouse on the Ports system database. However, they were not given access to the database company, and they dump some files or provide you with them. In one case, they were providing files. In another case, they provided some APIs where you need to call in a batch of thousands of records multiple times. It works very well with Azure Data Factory to pull the records, parse them quickly and post them in the database and data warehouse.

Compared to the on-premise SSIS, Azure Data Factory has better infrastructure. It also benefits my company because you can scale the solution up or down with different resources.

Azure Data Factory is also on a pay-as-you-go or pay-as-you-use model, which is suitable for the company because my company only pays for its usage or requirement.

The solution is also very user-friendly, and the Azure Data Factory support team responds quickly whenever my team has a loading issue.

ADF allowed us to extract Google Analytics data (via BigQuery) without purchasing an adapter.  

It has also helped with establishing how our team can operate within Azure using both PaaS and IaaS resources and how those can interact. Rolling out a small data factory has forced us to understand more about all of Azure and how ADF needs to rely upon and interact with other Azure resources.

It provides a learning ground for use of DevOps Git along with managing ARM templates as well as driving the need to establish best practices for CI.  

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.

Azure Data Factory allows us to create data analytic stores in a secure manner, run machine learning on our data, and easily adapt to changing schema.

I can do everything I want with SSIS and Azure Data Factory.

The company is experiencing significant benefits as one of our customers is successfully implementing the solution we provide. We offer support to the customer in utilizing Azure Data Factory, and their satisfaction level is quite high.

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

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.

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.

This solution has allowed me to quickly get analysis, sales data, supply chain data, and eCommerce data.

It has helped us to take care of a lot of our analytics requirements. We are running a few analytics models on Data Factory, which is very helpful.

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.

Prior to this, we did not have a proper data warehousing solution. Instead, we had segregation between different tools like Oracle Data Warehouse, Exadata, and other products. Now, most of the tools that we have are from Microsoft, including Power BI, which has been rolled out throughout the organization. Synapse was the better choice for us to implement, as it has a lot of out-of-the-box connectors that we can utilize for data transformation and organization. 

There has been improvement in data resilience, in the way that we're moving the data from on-prem to cloud and vice versa.

The integration with the whole environment is smooth and transparent.

I think the main benefit of this solution is the ease of use, especially for companies that have come from an SSIS type of background where they are used to Microsoft tools. 

This solution will allow the organisation to improve its existing data offerings over time by adding predictive analytics, data sharing via APIs and other enhancements readily. It's as simple as extending the data pipelines with new modules and components. The solution is improving the organisation by offering something the organisation can grow with.