In this project, we developed a data processing pipeline using Kafka producers and consumers written in Java to efficiently analyze large datasets. The pipeline begins with Kafka producers, which ingest data from various sources and publish it to Kafka topics. Kafka consumers then process the data, performing necessary transformations and aggregations.
Once processed, the data is uploaded to Databricks, a collaborative platform for data analytics. Within Databricks, the data is further analyzed using Python, leveraging its powerful libraries for data manipulation and visualization. We created detailed analytics and charts to provide insights into the dataset, enabling users to interact with and interpret the data effectively.
The combination of Kafka and Databricks ensures that the system can handle high-throughput data streams while providing robust analytics capabilities in a scalable environment. This project demonstrates the seamless integration of real-time data processing with advanced data analytics, delivering valuable insights from complex datasets.