The project was about to build a robust stream processing system capable of handling large-scale data ingestion and real-time querying. Their existing infrastructure lacked the scalability and query flexibility needed for high-volume analytical workloads. The goal was to design a system that could efficiently collect, transform, and expose streaming data in a queryable format across distributed environments.

The client needed a customized solution to ingest real-time data streams, process them efficiently, and make them queryable through standard interfaces. They also required an architecture that supported scalability and low-latency performance, with seamless integration into their existing analytics and reporting stack.

To meet these goals, Oodles provided the following solutions:

• Developed a modular stream processing pipeline using Python and Apache Kafka
• Integrated Apache Flink for real-time transformations and windowed operations
• Implemented a query layer on top of processed streams using Apache Druid
• Built connectors to ingest structured/unstructured data from various sources
• Designed a distributed architecture for horizontal scalability and fault tolerance
• Optimized message throughput and latency through asynchronous processing
• Delivered monitoring dashboards for operational observability using Grafana and Prometheus
• Documented the architecture and deployment strategy for client-side handover