The New MongoDB Connector for Apache Spark In Action: Building a Movie Recommendation Engine` is an introduction to the new native MongoDB Connector for Apache Spark, which provides higher performance, greater ease of use, and access to more advanced Spark functionality than other connectors. The connector exposes all of Spark's libraries, enabling MongoDB data to be materialized as DataFrames and Datasets for analysis with machine learning, graph, streaming, and SQL APIs. It also leverages MongoDB's aggregation pipeline and rich secondary indexes to extract, filter, and process only the range of data it needs. The Spark connector is aware of data locality in a MongoDB cluster, minimizing data movement across the cluster. This allows for faster processing times and better performance across large, distributed datasets. The example code demonstrates how to use the connector to build a movie recommendation system using Apache Spark's machine learning libraries, including how to read data from MongoDB into Spark, create a machine learning model, combine personal ratings with the rest of the dataset, generate user recommendations, and save them back into MongoDB for fast lookup in the future.