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Real-Time Data Processing Pipeline Modernization for Scalable Messaging Platform

scalac.io

Education

Information technology

Legacy Data Processing Challenges

Existing homegrown data pipeline struggles with high-volume data ingestion from CRM/SIS systems, leading to performance bottlenecks, inconsistent state management, and limited scalability. Current SQL-based approach causes expensive database operations and lacks robust error handling mechanisms.

About the Client

EdTech messaging platform enabling personalized two-way communication between educational institutions and students

Modernization Goals

Implement stateful stream processing for near real-time message handling
Improve data consistency through reliable state management
Enhance system scalability to accommodate growing user base
Reduce processing latency for time-sensitive communications
Implement robust error handling and monitoring capabilities

Core System Capabilities

Multi-source data ingestion from Kafka/RabbitMQ
Stateful stream processing with Redis caching
Dynamic data aggregation pipelines
Offset tracking for exactly-once processing
Parallel batch processing with ordered execution

Technology Stack

fs2

Kafka

Redis

Kafka Streams

Scala

cats-effect

System Integrations

CRM/SIS systems
Kafka ecosystem
Monitoring tools

Operational Requirements

Horizontal scalability to handle 10x data volume growth
Sub-second processing latency SLA
99.99% system availability
End-to-end data consistency guarantees
Comprehensive observability and monitoring

Business Impact

Enables 2x faster message processing at 40% lower operational cost, supporting 500k+ concurrent users while maintaining data integrity. Reduces processing delays from hours to seconds, improving student engagement rates through timely personalized communications and enabling proactive academic interventions.