Ataccama Reference Data Manager [EOL] vs Azure Data Factory comparison

"Really best product for this certain task area – to make reference data is manageable."

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

"I think it makes it very easy to understand what data flow is and so on. You can leverage the user interface to do the different data flows, and it's great. I like it a lot."

"If you have Azure as a cloud service and you want to perform ETL then Azure Data Factory is a product that I can recommend."

"Azure Data Factory is an integration tool, an orchestration service tool; it is for data integration for the cloud."

"It has built-in connectors for more than 100 sources and onboarding data from many different sources to the cloud environment."

"Azure Data Factory is a very easy to use ETL tool for loading and transforming data from one location to another."

"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."

"Creation process of services for on-line validation data (special services which are able to be used by another system) is too complicated."

"The main challenge with implementing Azure Data Factory is that it processes data in batches, not near real-time. To achieve near real-time processing, we need to schedule updates more frequently, which can be an issue. Its interface needs to be lighter."

"Compared to Informatica, it's really crude. I think it's a very crude solution."

"There is a need for better data transformation, especially since many people are now depending on DataBricks more for connectivity and data integration."

"They require more detailed error reporting, data normalization tools, easier connectivity to other services, more data services, and greater compatibility with other commonly used schemas."

"Data Factory would be improved if it were a little more configuration-oriented and not so code-oriented and if it had more automated features."

"As far as customer service and support with Azure Data Factory, we are not always satisfied with the response time."

"Technical support isn't the best, as it's a bit delayed at times. Whenever we need some urgent support, wherein we have to restart or something has stuck, it takes a bit of time."

"They should work on optimizing their licensing model and pricing structure."

"Pricing is comparable, it's somewhere in the middle."

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

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

"Data Factory is expensive."

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

"Azure Data Factory gives better value for the price than other solutions such as Informatica."

"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."

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