Azure Data Factory Pros and Cons

Best platform to control and orchestrate data flows from different wide sources and able to connnect differnt azure service using linked services Review collected by and hosted on G2.com.

Not able to transfer data to virtual machines Review collected by and hosted on G2.com.

I worked as a developer on a spark project where we process large amount of data using Azure data factory. It process data very fast and in efficient way. Also it supports a number of data sources. Review collected by and hosted on G2.com.

While working on a spark project I faced some challenges on advanced data processing tasks like filtration. On those lines it can be improved further. Review collected by and hosted on G2.com.

Azure Data Factory is a cloud service for data integration management that collects and links diverse data. It will be easier to understand if you imagine a line that trades immediately with collected and accumulated data (materials and raw materials). In addition, it can be linked to various systems to automate multiple information extraction processes required for data analysis. It can also be used for data integration, such as ETL and ELT. Azure Data Factory allows linking and using fragmented data through the development of ICT, accelerating the diversification of systems used in the enterprise from on-premises to the cloud. This platform makes business more efficient and convenient, as data is accumulated, analyzed and used efficiently, i.e., centrally managed. Transforms collected and stored data. Allows data to be used in a unified format to create graphs. Review collected by and hosted on G2.com.

I like Azure Data Factory because it can be used for conventional ETL and ELT with a few mouse clicks, as it can be flexibly scaled horizontally and vertically. Review collected by and hosted on G2.com.

The best thing is that it have lot of connectors for many on- premise servers and cloud servers and best part is data factory UI, where we can design complex etl pipelines by simply drag and drop the activities . Review collected by and hosted on G2.com.

I haven't faced any challenges with ADF , however there are some limitations where we can't perform some activities directly like nested control and also for complex transformation Adf mapping data flows not that much effective, but it's a best product. Review collected by and hosted on G2.com.

Implementing pipelines is very easy as it drag and drop Review collected by and hosted on G2.com.

connectivity datasources which only allow JWT is challenging and some data scenarios cannot be implemented Review collected by and hosted on G2.com.

You have multiple options based on your use cases and it all works. You just need to know when to use what. There's the commonly used Sql Server(which is now hosted on cloud), then you have Sql Warehouses, that they renamed to Synapse and added a whole lot of great functionalities which is appreciated. Review collected by and hosted on G2.com.

Their constant renaming and rebranding of stuff is just mind-boggling. Sql warehouses became Azure Synapse. If you ask anyone what Synapse is, they will give you an answer different from another answer that you might get and both of them would be correct. MS just needs to name their products correctly and stick to it without any redundancies. Review collected by and hosted on G2.com.

We use Azure data factory to pull data from multiple sources and import it into our data warehouses. We have many disparate data sources that rarely share a similar format. Using ADF, we can pull in the data automatically, normalize it, run queries against the current DW, import it into our DW, and archive files in cold storage. It's truly a lifesaver for anyone who prefers points and clicks to code. Review collected by and hosted on G2.com.

The learning curve can be pretty steep to learn how to use ADF. I used YouTube videos to supplement my knowledge, which was quite helpful. Once you get the concepts down, it's easy to apply to other projects, but finding out where to start can sometimes be daunting. Review collected by and hosted on G2.com.

Adf is an ETL tool provided by MS the best thing that I like is it is easy to use and we can easily connect this using link services with third-party resources to fetch the data. We can create a separate dataset for source and sink and using pipeline activity we can implement any transformation and get the required result using the sink directory. We have direct connectivity with databricks using the dbx workspace token we can connect our databricks notebooks using ADF. Review collected by and hosted on G2.com.

There is no major dislike but some points that I wanted to highlight are it has limitations in one pipeline we can create up to 40 activities and sometimes I faced any kind of strange error or a low server pipeline gets automatically failed without any hard reason so sometimes if we have prescheduled pipeline it may cause the data loss. Review collected by and hosted on G2.com.

Ease to making and deploying the data pipeline in Azure Data factory and best for all migration of large of amount of database . Review collected by and hosted on G2.com.

Not for beginners' use, and also it's taking lots of time for migration Review collected by and hosted on G2.com.

The best part of the azure data factory is we can perform various tasks like copy data from one platform to azure just like that. E.g Database replication, S3 bucket to azure blob storage.etc. It has the jobs which we can schedule based on our requirement and the drag and drop features which makes the UI and Administrator to easily work on the data factory Review collected by and hosted on G2.com.

We haven't faced any challenges with DataFactory. However, we are looking for more integration option must available. Review collected by and hosted on G2.com.