Building a Real-Time Data Pipeline with Kafka, MongoDB, and BigQuery

In this article, we’ll build a real-time data pipeline that uses Kafka as the backbone for data streaming, MongoDB as an operational database, and BigQuery for analytics. The goal is to provide a solution for processing real-time events and storing them for operational and analytical use cases.

The setup involves:

• Kafka for streaming real-time data.
• MongoDB to store operational data for use in applications.
• BigQuery is used to run analytical queries on the ingested data.

Below, we’ll break down each component, the architectural understanding, Docker setup, and the steps in building the entire pipeline.

Architectural Understanding

A real-time data pipeline involves multiple components working together to collect, process, and analyse data as it is generated. The architecture for this pipeline includes:

Data Pipeline

1. Kafka: Acts as the central data streaming platform, enabling communication between different components.

• Producers push data into Kafka topics.
• Consumers read data from these topics to perform various tasks.