A JupyterLab notebook was created to leverage MongoDB data in conjunction with PySpark, an open-source general-purpose cluster-computing framework that efficiently processes large-scale data. The notebook loaded financial security data from MongoDB using the MongoDB Spark Connector and PySpark, calculated a moving average based on the price of the stock security, and updated the data in MongoDB with the new calculation. The environment was set up to include a MongoDB cluster, an Apache Spark deployment, and JupyterLab, allowing for seamless integration and ad-hoc queries. The example demonstrates how easy it is to integrate MongoDB data within a Spark data science application, showcasing the capabilities of the MongoDB Connector for Spark.