Azure Data Factory vs WhereScape RED comparison

"The solution can scale very easily."

"Azure Data Factory became more user-friendly when data-flows were introduced."

"For me, it was that there are dedicated connectors for different targets or sources, different data sources. For example, there is direct connector to Salesforce, Oracle Service Cloud, etcetera, and that was really helpful."

"Powerful but easy-to-use and intuitive."

"Data Factory's best features are connectivity with different tools and focusing data ingestion using pipeline copy data."

"Data Flow and Databricks are going to be extremely valuable services, allowing data solutions to scale as the business grows and new data sources are added."

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

"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."

"This is a fantastically robust DW tool that will make you at least 10 times faster in producing a DW."

"WhereScape is really helpful in terms of architecture data. Everything is one of automation. Two people can do thousands of tables in one day or two. It saves a lot of time."

"It has a built-in automatic scheduling environment."

"Quickly develops a data warehouse for our organization with documentation and can track back/forward features."

"Data transformations and rollups are easy to accomplish."

"Their support staff are very knowledgeable, courteous, and professional. I feel their support staff go above and beyond to assure their customers are satisfied."

"WhereScape's deployment package is a fantastic feature. The application allows for selecting specific objects that you would like to deploy from one environment to another rather than deploying the entire database."

"I found the initial setup very easy."

"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 user interface could use improvement. It's not a major issue but it's something that can be improved."

"The solution needs to be more connectable to its own services."

"Lacks a decent UI that would give us a view of the kinds of requests that come in."

"There is room for improvement primarily in its streaming capabilities. For structured streaming and machine learning model implementation within an ETL process, it lags behind tools like Informatica."

"Data Factory's monitorability could be better."

"The thing we missed most was data update, but this is now available as of two weeks ago."

"No support for change data capture or delta detection - that must be custom coded ."

"They need a more robust support center. It has been a bit difficult to find solutions to problems that are out-of-the-box."

"The solution can be a little more user-friendly on enterprise-level where people use it."

"The scheduled jobs which are run by the WhereScape scheduler seem to be a strangely separate animal. Unlike all other WhereScape objects, jobs cannot be added to WhereScape projects. Also, unlike all other objects, jobs also cannot be deleted using a WhereScape deployment application."

"The ability to execute SSIS projects within WhereScape would be nice because we have a lot of packages that are too cumbersome to recreate."

"Jobs cannot be deleted via the deployment package. When deploying from dev to QA or production, a job has to be retired. The job has to be manually removed from the target environment."

"It could use a tool to diagnose what is missing from the environment for WhereScape to install successfully."

"Customization could be better."

"It's not particularly expensive."

"Pricing appears to be reasonable in my opinion."

"My company is on a monthly subscription for Azure Data Factory, but it's more of a pay-as-you-go model where your monthly invoice depends on how many resources you use. On a scale of one to five, pricing for Azure Data Factory is a four. It's just the usage fees my company pays monthly."

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

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

"The solution is cheap."

"I rate the product price as six on a scale of one to ten, where one is low price and ten is high price."

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

"Factor in the price of specialized consulting who know this product. They're hard to find and expensive."

"Speed to market of a warehouse solution at a relatively inexpensive price point."

"Our company purchased a corporate unlimited license."

"ROI is at least 10 times."