Bodo is a new scalable analytics platform that offers automatic parallelization and optimization of Python analytics code with the addition of a simple decorator. The author tested Bodo on a popular data analytics example, the Monte Carlo approximation of Pi, which considers a square with an inscribed circle and estimates the probability of a random point being in the circle as π/4. By adding the `@bodo.jit` decorator to the Python code, Bodo parallelizes and optimizes the function automatically, allowing it to run on multiple processors using mpiexec. The author benchmarked the performance of Bodo against Numpy on an AWS EC2 instance with 8 physical cores and a MacBook Pro with 6 physical cores, showing significant improvements in run time with increased core utilization. Bodo is efficient and runs directly on physical cores, making it suitable for large-scale data analytics applications. The author plans to share more advanced benchmarks in future blog posts and invites readers to test Bodo out for themselves or contact them to discuss their use case and dataset.