The Challenge
FunnelBeam needed a high-throughput data platform capable of ingesting, enriching, and serving company intelligence data at scale. Sales teams needed real-time access to enriched prospect profiles, but the data pipeline required complex orchestration across multiple data sources, search indices, and delivery channels.
Our Approach
Daiviksoft built a multi-language, microservices-based data platform spanning Python, Node.js, and Go. The system uses Apache Airflow for orchestrating data enrichment pipelines, Kafka for event streaming, and Elasticsearch for fast full-text search across millions of company records. The platform includes a Django-based backend, Flask APIs for specific enrichment services, and a React-based portal for end users.
Key Deliverables
- Multi-source data ingestion and company enrichment pipeline
- Real-time event streaming with Apache Kafka
- Full-text search across company records via Elasticsearch
- Airflow-orchestrated ETL workflows for data processing
- Multiple environment support (Dev, Demo, Vagrant, Production)
- Infrastructure as Code with Ansible and CloudFormation
- React-based sales intelligence portal
Results & Impact
FunnelBeam’s platform processes and enriches company data at scale, providing sales teams with actionable prospect intelligence. The microservices architecture allows individual components to scale independently based on load, and the Airflow pipelines ensure data freshness across the platform.