Delivering Faster Insights on Emergencies for RapidSOS
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RapidSOS is a safety platform that securely connects life-saving data from over 540 million connected devices, apps, and sensors directly to 911 and first responders worldwide. Founded in 2012, the company aims to modernize emergency response systems by delivering real-time, incident-specific data that enhances situational awareness and shortens response times globally.
The Challenge
In 2024, RapidSOS set out to upgrade its data backbone for emergency response—moving from delayed, brittle feeds to a real-time, reliable platform that could integrate signals from 540M+ connected devices. Before the project, ingestion lived across 20+ fragmented pipelines with tangled dependencies and legacy orchestrators, creating operational risk and frequent bottlenecks. Critical datasets refreshed anywhere from 24 to 72 hours, leaving teams without timely situational awareness when seconds mattered. The arrangement also made it hard to onboard new sources or expand to new geographies, and inconsistent data quality eroded trust across stakeholders. RapidSOS needed a single, governed ingestion layer with low-latency processing, standardized pipelines, and robust observability—so life-saving information could arrive fresh, complete, and on time.
The Solution
Muttdata rebuilt RapidSOS’s data backbone to run low-latency ingestion and analytics as signals arrive in the platform. We consolidated 20+ fragmented pipelines into Databricks Workflows, using Auto Loader for incremental loads, Delta Lake transactions and schema enforcement for consistency, and embedded freshness checks, data-quality tests, and alerting. Raw 911 call files from ECCs across states were onboarded from AWS storage, cleaned, enriched with new metadata, and standardized into a governed CDR Calls table—cutting data lag 80%, improving reliability..
Additionally, the modules were implemented within RapidSOS’s cloud and governance standards and integrated seamlessly with existing products and data consumers. Beyond building the system, we co-designed workflows with the RapidSOS team—modernizing operations today and laying a foundation for future AI-driven analytics.
The improvements achieved through our collaboration had both immediate technical and business benefits: Reduced data freshness lag:
The calls table update time decreased from 1 day to under 4 hours, providing RapidSOS with fresher and more reliable emergency data.
Stronger operational workflows: Migration to Databricks Workflows enhanced orchestration resilience, reduced failures, and improved visibility across the ingestion lifecycle.
Improved data governance and quality: We implemented enhanced data validation, monitoring, and lineage tracking processes, enabling more robust governance and greater trust in analytics outputs.
Cost optimization: Serverless compute adoption and cluster right-sizing initiatives reduced processing costs by an estimated 5–15%.
Data foundation for future AI initiatives: With better quality, faster refresh rates, and broader data coverage, RapidSOS is now positioned to scale the development of AI agents and predictive analytics.
