Integrating MongoDB and Apache Kafka allows you to build a real-time data pipeline that captures, processes, and delivers data as it's generated. Here's how you can construct this pipeline: 

• Data Producer: Begin by identifying the sources of data you want to capture in real-time. This could be user interactions on a website, IoT devices, or application logs. Configure Kafka producers to send data to Kafka topics. 
• Kafka Cluster: Set up an Apache Kafka cluster to act as the central hub for data streaming. Kafka stores the data in topics, and each topic represents a specific data stream. 
• Data Transformation: Use Kafka Streams or other stream processing frameworks to perform real-time data transformations and enrichments on the data streams. This could include aggregations, filtering, and joining with other data sources. 
• MongoDB Connector: Implement a Kafka Connect MongoDB Sink Connector to stream the processed data from Kafka topics to MongoDB collections. This allows you to persist the real-time data in MongoDB for further analysis. 
• Data Analytics: Utilize MongoDB's querying and aggregation capabilities to analyze the real-time data stored in the database. This could involve generating reports, visualizations, and insights to support decision-making.