Azure Data Factory Pros and Cons

The best features according to me are Hybrid Data Integration, Data Movement, Orchestration and Scheduling and Integration with other Azure Services. Review collected by and hosted on G2.com.

According to me there are no downsides of ADF. Review collected by and hosted on G2.com.

In my experience, ADF comes out to be a champion in its ease of use quality. The minimal coding requirements, drag and drop features has been incredibly useful. Not to mention the powerful debugging it offers. Its capabilities to integrate across various data sources is nothing but a life saver Review collected by and hosted on G2.com.

While ADF offers simplified ETL solution, it also creates challenges in few areas. Such as complex transformations using data flow. Debugging in Azure data flow has been a frustrating step in all my pipeline development. This particular feature is rather inefficient when it comes to transform huge volume of data. Add in ambiguous errors and you got your biggest nightmare Review collected by and hosted on G2.com.

1. ADF can handle large volumes of data and support diverse formats. Whether it is RDBMS, NoSQL,File system, app or services, we can connect with them to ADF and do complex data processing, data integration.

2. visual workflow orchestration simplifies the creation and management of complex data pipelines.

It is very easy to build a pipeline in the Azure data factory. Drag-and-drop features make it simple for the user. Plus, every pipeline structure that we have built is written in JSON format which is really amazing when it comes to changing and copying the pipeline structure from one ADF to another ADF.

3.the integration with Azure services like Azure Data Lake Storage, Azure Synapse Analytics, and Azure Databricks enhances our data engineering pipeline's overall capabilities and productivity. Review collected by and hosted on G2.com.

1.Microsoft should improve on ADF documentation and availability of comprehensive examples and tutorials for different scenarios. Different companies have different scenarios and different pipeline structure.In order to to customize what they want, ADF should provide more basic documentation and tutorials that are sharing across orgs.

2. Sometimes it has bugs when we are duplicating one data pipeline and replace necessary datasets and linked service in duplicated one. When we run duplicated pipeline, it is still cached the previous data pipeline and can’t see the duplicated data. That time, we have to refresh the browser which is annoying and time-consuming. Review collected by and hosted on G2.com.

It hekps in design a pipeline using different functionalities that can accomodate source and target location and implement the function based on the pipeline created.Also it has execllent feature like monitoring the pipeline and have an option to rerun the pipeline which reduces the rework on creating a duplicate data refresh pipeline. Review collected by and hosted on G2.com.

It requires a basic knowledge on the app on how to create the pipeline ,how we can access the datalake storage areas and map it to the source and sink on the new pipeline. Review collected by and hosted on G2.com.

The best thing in ADF is the data flow debug where we can direct check the data flow output in every task and find the errors with pipeline run. Review collected by and hosted on G2.com.

Deployment to prod but it's not too dislike but it's hard to deploy to prod from dev . We have to use cl cd pipelines etc. Review collected by and hosted on G2.com.

Data Integration and Orchestration: ADF allows you to efficiently integrate and orchestrate data from various sources, both on-premises and in the cloud. It provides a visual interface for designing data pipelines, making it easier to define and manage complex data integration workflows.

Broad Data Source Support: ADF supports a wide range of data sources, including Azure services, on-premises databases, SaaS applications, and various file formats. This flexibility enables you to extract, transform, and load (ETL) data from diverse sources, making it suitable for heterogeneous data environments.

Scalability and Performance: ADF leverages the scalability and power of the Azure platform to handle large volumes of data and process it at scale. It can parallelize data processing activities, optimize resource utilization, and provide efficient data movement capabilities, leading to improved performance. Review collected by and hosted on G2.com.

Till now I havnt found any cons of ADF in my 4 years of IT experience wotking with ADF Review collected by and hosted on G2.com.

The most helpful thing is that it helps in loading huge amounts of data with just a trigger of a pipeline and a job. It is on the cloud and base don microsoft it becomes easy Review collected by and hosted on G2.com.

SOmetimes it becomes difficult to comprehend the errors due to which the data pipeline fails. Even after looking on internet doesn't help so may be the error message can be improved which helps users to comprehend and easily resolve it. Review collected by and hosted on G2.com.

It is easy to use for data engineers because of its drag and drop feature. Its a powerful no code etl tool. it can easily perform orchestration and can build robust data engineering pipelines. Review collected by and hosted on G2.com.

Due to its no code feature there is a limitation in doing complex transformations in it. And also CI/CD of data factory pipelines is very complex as chery picking is not available. Review collected by and hosted on G2.com.

Easy UI, love the transformation building using data flow. Easy integration with data bricks and api gateways Review collected by and hosted on G2.com.

nothing really. More ease with global parameters Review collected by and hosted on G2.com.

The Best thing about ADF is we can integrate almost every data source into it. I liked the Data flows the most as it provides a Spark engine, and we can debug and preview the data without actual execution. Review collected by and hosted on G2.com.

The pane where we create pipelines and data flows requires a big screen and becomes complicated if we add more components in the pipeline on a small screen device. Review collected by and hosted on G2.com.