Azure Data Factory vs Collibra Catalog comparison

"The most valuable features of the solution are its ease of use and the readily available adapters for connecting with various sources."

"The data flows were beneficial, allowing us to perform multiple transformations."

"An excellent tool for pipeline orchestration."

"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."

"I like the basic features like the data-based pipelines."

"The data is more scalable."

"When it comes to our business requirements, this solution has worked well for us. However, we have not stretched it to the limit."

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

"Collibra Catalog's best feature is the data quality checker."

"Collibra Catalog allows us to automate metadata management, significantly saving time, effort, and finances."

"The workflows allow the creation of custom workflows based on needs."

"Gartner identifies Collibra Catalog as the leader, which aligns with our observations."

"Using lineage and Collibra Catalog has helped me overall improve the trust and transparency regarding data origin and transformation."

"Except for data quality, everything is perfect."

"Collibra Catalog is simple to use and user-friendly for those who are not technically inclined since it is easy to find while also easy to see data lineage diagrams."

"Collibra Catalog has significantly enhanced data governance and compliance for our team, primarily through its valuable feature of endpoint lineage enabling visual representation of the data."

"Data Factory's performance during heavy data processing isn't great."

"Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities. This is something that is just not fully covered. Additionally, the integration could improve for other tools, such as Azure Data Catalog."

"The inability to connect local VMs and local servers into the data flow is a limitation that prevents giving Azure Data Factory a perfect score."

"Some prebuilt data source or data connection aspects are generic."

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

"Areas for improvement in Azure Data Factory include connectivity and integration. When you use integration runtime, whenever there's a failure, the backup process in Azure Data Factory takes time, so this is another area for improvement."

"There is no built-in function for automatically adding notifications concerning the progress or outline of a pipeline run."

"Sometimes I need to do some coding, and I'd like to avoid that. I'd like no-code integrations."

"If it can become more user-intuitive and work on integrating with communication platforms like Slack or Teams, it would significantly help business users."

"There is an issue with Collibra Catalog's pricing model, especially for organizations with many databases, as the initial package comes with a limited number of connectors."

"Collibra Catalog could improve its automation to increase the efficiency of the software."

"One of the very key drawbacks is that automation for access provisioning is not available. If I discover a data set or data product in the marketplace and want to access the data, this feature doesn't exist at all."

"The tool's overall functionalities need to improve since, nowadays, many tools, from a business perspective, are easy to use."

"A key area for improvement in Collibra Catalog lies in its integration capabilities, particularly with a broader range of sources."

"If the price is a bit reduced, that would be better."

"In Collibra Catalog, the main area that has room for improvement is the search functionality. It should be more natural language oriented instead of searching for exact names."

"The price is fair."

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

"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 appears to be reasonable in my opinion."

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

"While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products."

"It's not particularly expensive."

"The product is highly priced compared to other vendors."

"Collibra offers a per-user licensing model."

"Collibra Catalog is fairly priced - I would rate their pricing seven out of ten."

"I think they can bring a few more features and align better with other quality products."