Azure Data Factory Reviews & Product Details

What’s great about ADF is how easy it makes connecting all my different data sources whether they’re on-prem or in the cloud without having to write a lot of code. I can visually drag and drop to build complex data pipelines, automate repetitive data tasks, and seamlessly move and transform huge volumes of data as my projects grows. I can also monitor and manage everything from one place, which takes a lot of stress out of dealing with data integration and makes life much simpler for everyone involved. Review collected by and hosted on G2.com.

The things I dislike most about ADF are usually around its complexity and limitations. Debugging pipelines is often tricky and can be frustrating, especially when something fails, there aren’t many tools for step-by-step troubleshooting. If my workflow is complex, managing and understanding everything can get overwhelming with so many moving pieces and screens to keep track of. Additionally, its integration with non-Azure services is quite limited, so I may run into roadblocks if I need to connect other platforms or work in a Azure cloud setup. And while it handles simple tasks well, customizing things beyond standard connectors or data flows often means writing extra code or finding difficult workarounds. Review collected by and hosted on G2.com.

It enables scalable data integration between on-premises and cloud sources without intensive infrastructure management. Mapping data flows make complex transformations efficient and intuitive. I also appreciate the monitoring and control features, which provide clear visibility into pipeline executions. Review collected by and hosted on G2.com.

Debugging and error messages are not always clear, which can make troubleshooting time-consuming. Pricing can also be difficult to estimate, as the serverless model relies on activity execution and data movement. Review collected by and hosted on G2.com.

Its ability to orchestrate and automate complex data integration workflows at scale. Its low-code, visual interface makes it easy to build and monitor pipelines, while its seamless integration with a wide range of data sources—both on-premises and cloud—enables reliable end-to-end data movement. Additionally, features like built-in scheduling, monitoring, error handling, and integration with other Azure services make it a robust and scalable solution for enterprise data engineering needs. Review collected by and hosted on G2.com.

While Azure Data Factory is a powerful orchestration tool, some limitations include limited debugging capabilities for complex pipelines and the lack of advanced transformation features compared to dedicated data processing engines like Spark. Version control and CI/CD integration can also feel less intuitive, especially for large teams, and troubleshooting performance or runtime issues can sometimes require switching between multiple monitoring views. Additionally, costs can increase as pipelines grow in complexity and frequency, requiring careful optimization and governance. Review collected by and hosted on G2.com.

Azure Data Factories have been heavily implemented in our Organization and most of them are used for ETL/ELT, parsing data, etc

I use the connectors for storage i.e. Blob and ADLS connector for data ingestion and data lake architecture. Review collected by and hosted on G2.com.

ADFs have latency hence real time streaming is an issue. Review collected by and hosted on G2.com.

Schedules and various types of schedules available in ADF. Review collected by and hosted on G2.com.

We can’t see all our pipeline activities in single page while monitoring. Also data gets erased after 50 days Review collected by and hosted on G2.com.

It's low code interface and integration flexibility is what I like best about azure data factory. You can connect to a wide range of data sources and create complex data pipelines visually without writing heavy code Review collected by and hosted on G2.com.

The debugging experience on the portal can be slow especially for large data flows. Also data flow mapping UI could be more responsive. Review collected by and hosted on G2.com.

I find Azure Data Factory user friendly - it's easy to navigate and find what I am looking for. Review collected by and hosted on G2.com.

Nothing I particularly dislike - haven't used many similar tools. Review collected by and hosted on G2.com.

Orchestrating processes by using various activities within Data Factory. Review collected by and hosted on G2.com.

complex pipeline structure and hard to track Review collected by and hosted on G2.com.

Linked services to connect to all types of services Review collected by and hosted on G2.com.

Simple control flow becomes very complex to achieve Review collected by and hosted on G2.com.

1. Easy to use (Gives access to read data from multiple sources and data of multiple format)

2. Linked services gives lots of connection with other platform almost making it cross platform

3. Excellent ETL tool with lots of inbuilt activities.

4. Connection with Databricks notebook and it's integration is top class.(In case of complex ETL works we can use Databricks and call it directly to ADF pipeline)

5. Data Flow are easy to implement and very minimal code is required. Review collected by and hosted on G2.com.

Fails to perform complex transformation.

Limitation of not able to perform more operations than Int32. Review collected by and hosted on G2.com.