Azure Data Factory

Well-suited Scenarios for Azure Data Factory (ADF):
When an organization has data sources spread across on-premises databases and cloud storage solutions, I think Azure Data Factory is excellent for integrating these sources.
Azure Data Factory's integration with Azure Databricks allows it to handle large-scale data transformations effectively, leveraging the power of distributed processing.
For regular ETL or ELT processes that need to run at specific intervals (daily, weekly, etc.), I think Azure Data Factory's scheduling capabilities are very handy.

Less Appropriate Scenarios for Azure Data Factory:
Real-time Data Streaming - Azure Data Factory is primarily batch-oriented.
Simple Data Copy Tasks - For straightforward data copy tasks without the need for transformation or complex workflows, in my opinion, using Azure Data Factory might be overkill; simpler tools or scripts could suffice.
Advanced Data Science Workflows: While Azure Data Factory can handle data prep and transformation, in my experience, it's not designed for in-depth data science tasks. I think for advanced analytics, machine learning, or statistical modeling, integration with specialized tools would be necessary.

In a data pipeline, you will be able to add different kinds of activities for example connect from your on-premise SFTP and move CSV files to storage accounts. As well data factory has its own data flow if you are an ETL developer who experimented with maybe you have worked with SSIS, thus, you will start quickly with this new feature of the data factory.

Well Suited:

• Offers low code/no code features executes against spark pool.
• Batch processing features, Tight coupling with Databricks & ETL jobs.
• Offers Logic apps & Azure functions invoking API.

Less Appropriate:

• Not much inherent features of Stream analytics (Liasing Azure Stream analytics to DF might be good option).
• Advanced load options viz . Upsert type operations missing.

Azure Data Factory can perform better with Azure services and can easily do cloud migrations from on-premises services like SQL Server. It has a limited set of functionalities to transform data using SSIS integration services and data flow.

If you know a bit about database management everything is pretty easy, based on my personal experience. You can build a lot of things entirely in design, even if you do not know all the syntax, [since] having ready-made templates simplifies everything to get started and build the first pipeline.

It works better than other tools from the same range, it has a beautiful UI and it makes work easy. Its also very easy to integrate with other tools, tools, apps and ecosystems.

If you are just getting started and all your data is resident in the Azure cloud, then Azure Data Factory is likely to work fine without having to jump through too many hoops. However, in a hybrid environment (which is most of them these days), ADF will likely need a leg up. It works well for scheduling and basic scheduling/orchestration tasks, but the feature set is not at a level with SSIS (which has been around for 15 years so...). As ADF now supports deploying SSIS, it is also a good candidate if large amounts of your data are resident in the Azure cloud and you have an existing SSIS investment in code and licensing. We are using it in a hybrid fashion for the data warehouse and will slowly transition over to ADF as the feature set improves. We are also using it for cloud-native applications that only require supplemental data from on-prem resources.