Powering Real-time Queries on High-Throughput Telematics Data with Apache Iceberg

Executive Summary

A fast-growing telematics data platform processes terabytes of GPS, vehicle diagnostics, and sensor data every day. With hundreds of integrations and strict customer-facing SLAs, operating Apache Iceberg at scale became a limiting factor.

Manual maintenance, frequent updates, and write contention introduced performance risk and operational drag. As data volumes accelerated toward petabyte scale, the team needed Iceberg to remain fast and reliable without adding dedicated maintenance engineers.

By adopting the Ryft Platform, the company automated Iceberg operations across ingestion, storage, and query workloads. Ryft eliminated customer-visible performance issues, powered real-time sub-second queries, reduced operational overhead by 95%, and enabled the platform to scale with confidence.

The Challenge: Operating Iceberg Under Real-Time Load

The platform aggregates dense telematics data from hundreds of external systems into a unified API consumed by downstream analytics and operational tools. The workload includes:

• Continuous streaming and CDC ingestion
• High rates of updates and deletes
• Customer-facing tables serving 30K–40K requests per second
• Strict latency and reliability requirements
• Predictable Iceberg operations at PB scale
• Centralized visibility and GDPR compliance without custom tooling

At scale, Iceberg maintenance became fragile and manual:

• Thousands of small files are created daily
• Frequent position deletes requiring rewrites
• Immediate post-ingestion compaction needed for critical tables
• Limited engineering capacity for operational work

The goal was simple: take Iceberg operations off the team’s plate without compromising data safety or customer-facing performance.

Why Ryft: Intelligent Iceberg Management Platform

The team didn’t need more scripts - they needed Iceberg to run itself under real production load. The Ryft Platform serves as an operational control plane for Iceberg, learning from how tables are written and queried and automating the work that engineers typically manage manually. It handles compaction, delete rewrites, and write coordination in ways that are aware of streaming, CDC, and high-throughput ingestion, while providing clear visibility into ingestion health, storage layout, and query behavior. The Ryft Platform adapts as workloads change, reduces commit conflicts, and enforces safety by default—allowing the team to scale Iceberg to petabyte volumes without added operational complexity.

Adaptive Performance Optimization at Scale

Telematics workloads are bursty and write-heavy. Static tuning couldn’t keep up.
Ryft introduced workload-aware optimization that continuously adapts to real usage:

• Immediate post-ingestion compaction for critical tables
• Dynamic resource allocation to reduce write contention
• Automated handling of position deletes
• Optimization designed for streaming and CDC
• Storage tiering to lower costs for cold data

Results

• 85% faster compaction on customer-facing tables
• Consistent real-time sub-second query performance
• Infrastructure ready for petabyte-scale growth

Visibility, Control, and Governance Across the Lakehouse

Before Ryft, diagnosing performance issues required guesswork across logs and metrics. Ryft delivered unified visibility across ingestion, storage, and queries, with clear cost and performance attribution to specific tables and workloads.

Governance was embedded directly into operations, including role-based access control, auditing, automated lifecycle management, and backup and recovery—without slowing teams down.

Business Impact

Operational

• 95% reduction in manual maintenance
• Zero-touch optimization for critical tables
• 99.9% uptime for automated operations

Customer

• Sub-second performance under sustained load
• No ingestion-related delays impacting APIs
• Improved reliability of customer-facing APIs

Growth

• Platform ready for 1PB+ scale
• No additional infrastructure headcount
• Engineers refocused on core product development

Key Metrics

• Daily data processed: Multi-TB → 1PB+ ready
• Engineering overhead: –95%
• Compaction speed: 85% faster
• Query latency: <1s at 30K+ req/sec
• Operational uptime: 99.9%

Why It Matters

Iceberg enables modern lakehouse architectures, but operating it at scale is where teams struggle. The Ryft Platform automates the operational work that slows teams down, delivering adaptive optimization, real visibility, and safety by default. So data and infrastructure teams can scale high-throughput, customer-facing workloads without sacrificing performance or focus.