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Queryable Stream System
LANCER
Lancer :
Ideal for quick, focused engagements
Team: ≤ 2 resources
Duration: ≤ 3 weeks
Indicative Budget: ≤ $3,000 USD*
Use Cases: MVPs, prototypes, simple websites, bug-fix sprints
Indicative Pricing: ~$15–$25/hr per resource*
Growth :
Best suited for growing products or structured builds
Team: 2–5 resources
Duration: Up to 4 months
Indicative Budget: Up to $30,000 USD*
Use Cases: Feature-rich web apps, integrations, custom dashboards
Indicative Pricing: ~$15–$35/hr per resource*
Enterprise :
Tailored for high-scale, complex or long-term initiatives
Team: 5+ resources
Duration: More than 4 months
Indicative Budget: Starts from $30,000+ USD*
Use Cases: End-to-end platforms, SaaS products, compliance-heavy systems
Indicative Pricing: ~$25–$50/hr depending on roles*
(Development)
Project Description
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.
Scope Of Work
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.
Our Solution
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
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