SIVARO

Product Engineering Services USA: Scaling a SaaS Observability Platform to 100K Events/Sec

A US-based SaaS company, "TelemetryFlow," built a next-generation observability platform for cloud-native applications. Their initial success became their biggest challenge. As they onboarded enterprise clients, their custom-built data ingestion pipeline, designed for a modest 10K events per second, began to crumble under loads exceeding 50K events. Data loss became frequent, query latencies spiked to minutes, and their platform's reliability—their core value proposition—was failing. They needed expert product engineering services in the USA to rebuild their data infrastructure for massive, predictable scale without sacrificing developer agility.

The Breaking Point: A Data Pipeline in Distress

TelemetryFlow's platform ingested logs, metrics, and traces from thousands of microservices. Their monolithic ingestion service, coupled with a traditional PostgreSQL database for metadata and a time-series store, formed a fragile chain. The failure mode was systemic: under peak load, the ingestion service's queues would overflow, leading to dropped events. Concurrently, the metadata database became a severe bottleneck, causing cascading failures in event correlation and routing. Their 99.9% uptime SLA was consistently breached, and mean time to resolution (MTTR) for customer issues ballooned. Key metrics told a grim story:

Sivaro's Solution: A Re-Architected, Cloud-Native Data Mesh

Sivaro's product engineering team in the USA initiated a complete architectural overhaul. The goal was to decouple components, introduce intelligent buffering, and select purpose-built databases to create a resilient, scalable data mesh.

1. Event Ingestion & Stream Processing

We replaced the monolithic service with a cloud-native stack using Apache Kafka as the durable, high-throughput event backbone. We implemented Apache Flink for stateful stream processing, enabling real-time enrichment, aggregation, and routing of events before they hit the storage layer. This distributed approach eliminated the single point of failure. As noted in the Apache Flink documentation, its "ability to handle large volumes of data with low latency and high throughput makes it ideal for real-time analytics and data pipeline applications" [^1].

2. Metadata Management

The PostgreSQL bottleneck was addressed by migrating metadata to ScyllaDB, a Cassandra-compatible NoSQL database built for low latency and high throughput. Its shared-nothing architecture and mastery of SSD storage provided the linear scalability needed for metadata lookups at the scale of 100K+ events/sec. A benchmark from ScyllaDB's engineering blog confirms that "ScyllaDB can achieve single-digit millisecond p99 latencies even under workloads of millions of operations per second" [^2], making it a perfect fit for TelemetryFlow's dynamic metadata.

3. Time-Series & Object Storage

We implemented a tiered storage strategy. Hot, recent data was stored in TimescaleDB (a PostgreSQL extension) for fast, SQL-based querying. Cold data was automatically compressed and archived to Amazon S3, with a unified query layer (using Apache Iceberg) providing seamless access.

4. Platform & Orchestration

The entire pipeline was containerized with Docker and orchestrated via Kubernetes on AWS EKS, allowing for auto-scaling of every component based on custom metrics (like Kafka consumer lag). Infrastructure-as-Code using Terraform ensured reproducible, version-controlled environments.

Technical Outcomes & Measurable Impact

The new architecture was rolled out in phases over six months. The performance and business outcomes were transformative:

Engineering a Foundation for the Future

This engagement with TelemetryFlow exemplifies Sivaro's approach to product engineering services in the USA. We don't just patch problems; we co-engineer foundational systems that turn scalability from a constant threat into a competitive moat. By leveraging cutting-edge, open-source technologies within a cloud-native framework, we built a system that is not only performant today but also adaptable for the data volumes of tomorrow.

Is your SaaS product's data infrastructure holding back growth and reliability? Our team specializes in architecting high-performance, scalable systems for the most demanding environments.

Let's build a platform that scales with your ambition. Contact Sivaro's Product Engineering Team Today.

Explore More Case Studies:

  1. Apache Flink Documentation: "Concepts Overview." Apache Software Foundation. https://nightlies.apache.org/flink/flink-docs-stable/docs/concepts/overview/
  2. ScyllaDB Engineering Blog: "Benchmarking Cloud NoSQL Databases: ScyllaDB Cloud vs. Apache Cassandra." ScyllaDB. https://www.scylladb.com/2023/07/18/benchmarking-cloud-nosql-databases-scylladb-cloud-vs-apache-cassandra/