While MarkLogic is powerful, there are areas where I feel it could improve. When I started with MarkLogic, I found that its learning curve and developer experience are not very comfortable for beginners. Technologies such as XQuery are less common compared to Java and Python, so new developers take time to get comfortable with it. Improving documentation and modern tooling would greatly aid onboarding. Second, the cost and licensing can be a concern for smaller teams and startups. MarkLogic's enterprise status makes it less likely to be the first choice for those teams. While it supports deployment in the cloud, the experience could be more seamless compared to fully cloud-native databases. Overall, MarkLogic is excellent for enterprise use cases, especially where search and structured data need to work together, but improving developer experience and ecosystem support would enhance its efficiency. A couple of additional areas where MarkLogic could improve are around integration, performance tuning, visibility, and support experiences. While MarkLogic supports REST APIs well, integrating with the modern data ecosystem sometimes requires extra effort compared to other platforms, as out-of-the-box connectors are limited. Although performance is strong, understanding query behavior can be challenging, and debugging slow queries or analyzing indexing usage is not always transparent. Regarding support and documentation, response times can vary depending on the issue or server availability. More real-world examples and troubleshooting guides would enhance developer productivity. Improvements in integration and modern tools in XQuery, along with better observability, are necessary. Beyond what I mentioned earlier, there are a few additional areas I can point to. While MarkLogic supports powerful querying via XQuery and JavaScript, many developers are more comfortable with SQL. An intuitive SQL-like query support or a better abstraction layer would enhance adoption across teams. Furthermore, migrating from other databases, whether relational or non-SQL, requires effort in data transformation. Better migration tooling with automated data mapping would also make transitions smoother.
