Another overlooked feature of Julia is its built-in support for distributed computing. The language includes primitives for distributed arrays and parallel computing, making it easy to harness the full power of multicore processors and distributed computing clusters. This can lead to significant performance improvements for computationally intensive tasks.

Lastly, Julia has excellent support for interactive data visualization through packages like 'Plots' and 'Gadfly'. These libraries allow for the creation of high-quality, customizable plots and graphs, making it easier for developers to explore and present their data in a visually appealing way.

One lesser-known feature of Julia is its ability to seamlessly call C and Fortran code. This enables developers to leverage existing libraries and take advantage of the performance benefits offered by these languages. Additionally, Julia has a powerful metaprogramming capability that allows developers to generate code dynamically, opening up possibilities for creating flexible and optimized programs.

Julia also offers a lightweight task-based parallelism model through its 'tasks' system. Developers can easily spawn lightweight concurrent tasks, which can help improve performance and responsiveness in certain scenarios. This is especially useful in I/O-bound or event-driven applications.