Azure Data Factory vs Toad Data Point comparison

"From what we have seen so far, the solution seems very stable."

"The tool's most valuable features are its connectors. It has many out-of-the-box connectors. We use ADF for ETL processes. Our main use case involves integrating data from various databases, processing it, and loading it into the target database. ADF plays a crucial role in orchestrating these ETL workflows."

"This solution will allow the organisation to improve its existing data offerings over time by adding predictive analytics, data sharing via APIs and other enhancements readily."

"The solution has a good interface and the integration with GitHub is very useful."

"ADF is another ETL tool similar to Informatica that can transform data or copy it from on-prem to the cloud or vice versa. Once we have the data, we can apply various transformations to it and schedule our pipeline according to our business needs. ADF integrates with Databricks. We can call our Databricks notebooks and schedule them via ADF."

"The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed. The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem."

"One advantage of Azure Data Factory is that it's fast, unlike SSIS and other on-premise tools. It's also very convenient because it has multiple connectors. The availability of native connectors allows you to connect to several resources to analyze data streams."

"Data Factory's best feature is the ease of setting up pipelines for data and cloud integrations."

"The most valuable features of Toad Data are you could write a parameterized query and it wouldn't error out, it would give you the parameters that you could input. The auto-formatting feature is useful because it was great for keeping your queries neat and understandable. The auto comment, and uncomment toggles that you could do were convenient."

"The Connectivity and Connection Manager supports a broad number of connection types, and it is trivial for end-users to set up their own connections to sources."

"While it has a range of connectors for various systems, such as ERP systems, the support for these connectors can be lacking."

"Data Factory has so many features that it can be a little difficult or confusing to find some settings and configurations. I'm sure there's a way to make it a little easier to navigate."

"Some known bugs and issues with Azure Data Factory could be rectified."

"The number of standard adaptors could be extended further."

"User-friendliness and user effectiveness are unquestionably important, and it may be a good option here to improve the user experience. However, I believe that more and more sophisticated monitoring would be beneficial."

"The support and the documentation can be improved."

"The product could provide more ways to import and export data."

"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."

"Toad Data could improve by having additional features, such as query prediction. This could help someone who's not the strongest programmer. If the software could help them write queries correctly it would be very helpful, especially for small development teams or teams that lack the input skills necessary to write and program efficiently."

"On the scheduling server, some scheduled reports just sit there and never execute for the first time. After manually executing the first time, they run with no issues."

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

"The licensing cost is included in the Synapse."

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

"I would rate Data Factory's pricing nine out of ten."

"The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper."

"Azure products generally offer competitive pricing, suitable for diverse budget considerations."

"The pricing model is based on usage and is not cheap."

"The solution is cheap."

"The price of Toad Data Point was approximately $500 annually."

"The cost of this product is reasonable."