Azure Data Factory vs DBamp comparison

"Data Factory allows you to pull data from multiple systems, transform it according to your business needs, and load it into a data warehouse or data lake."

"The two most valuable features of Azure Data Factory are that it's very scalable and that it's also highly reliable."

"I find that the solution integrates well with cloud technologies, which we are using for different clouds like Snowflake and AWS"

"The security of the agent that is installed on-premises is very good."

"The overall performance is quite good."

"Feature-wise, one of the most valuable ones is the data flows introduced recently in the solution."

"It makes it easy to collect data from different sources."

"Azure Data Factory is a low code, no code platform, which is helpful."

"DBM allows integration with SQL Server, which is beneficial for Microsoft."

"Their support is good, very responsive despite being overseas."

"When working with AWS, we have noticed that the difference between ADF and AWS is that AWS is more customer-focused. They're more responsive compared to any other company. ADF is not as good as AWS, but it should be. If AWS is ten out of ten, ADF is around eight out of ten. I think AWS is easier to understand from the GUI perspective compared to ADF."

"Azure Data Factory could benefit from improvements in its monitoring capabilities to provide a more robust feature set. Enhancing the ease of deployment to higher environments within Azure DevOps would be beneficial, as the current process often requires extensive scripting and pipeline development. It is also known for the flexibility of the data flow feature, particularly in supporting more dynamic data-driven architectures. These enhancements would contribute to a more seamless and efficient workflow within GitLab."

"There aren't many third-party extensions or plugins available in the solution."

"The pricing model should be more transparent and available online."

"In the next release, it's important that some sort of scheduler for running tasks is added."

"It would be better if it had machine learning capabilities."

"A room for improvement in Azure Data Factory is its speed. Parallelization also needs improvement."

"Currently, our company requires a monitoring tool, and that isn't available in Azure Data Factory."

"DBM is a bit more complicated when compared to DataLoader, especially for more complex operations."

"DBM is a bit more complicated when compared to DataLoader, especially for more complex operations."

"The pricing is a bit on the higher end."

"The cost is based on the amount of data sets that we are ingesting."

"There's no licensing for Azure Data Factory, they have a consumption payment model. How often you are running the service and how long that service takes to run. The price can be approximately $500 to $1,000 per month but depends on the scaling."

"The licensing is a pay-as-you-go model, where you pay for what you consume."

"This is a cost-effective solution."

"The solution's fees are based on a pay-per-minute use plus the amount of data required to process."

"In terms of licensing costs, we pay somewhere around S14,000 USD per month. There are some additional costs. For example, we would have to subscribe to some additional computing and for elasticity, but they are minimal."

"Our licensing fees are approximately 15,000 ($150 USD) per month."