What is Quasar?

Quasar is a DBMS designed for continuous ingestion and real-time querying of numerical and time-series data at scale, including deep historical datasets.

The Challenge: Data at Scale

You’re collecting more data than ever, but you can’t use it in real time.

Data lag, bottleneck: your data is locked in.

Your systems force tradeoffs

Either you ingest at scale, or you query efficiently—but not both. As data grows, queries slow down, pipelines multiply, and everything becomes harder to operate.

You end up stitching together heterogenous systems

Historians, streaming systems, and analytics layers just to make the data usable: introducing latency, complexity, and constant maintenance.

● The Quasar Difference

Quasar is a distributed, column-oriented database built for numerical and time-series data, combining patented compression with low-level APIs to enable continuous ingestion and real-time querying at scale. All data remains accessible through SQL, APIs, and standard interfaces, without duplication or pipeline fragmentation.

High-throughput sustained workloads

Quasar is built as a distributed, column-oriented engine with an LSM-based write path optimized for very high ingestion rates. Smart indexing and data organization minimize write amplification while maintaining efficient query performance under sustained load.

  • No tradeoff between writes and reads
  • Stable under constant pressure

Efficient tiered storage at scale

Quasar combines a patented compression engine optimized for numerical data with tiered storage across NVMe and object storage. Data is transparently managed across storage layers, maintaining query performance while significantly reducing footprint and cost.

  • Makes large-scale data economically viable
  • Storage and query performance aligned

Real-time + historical in one system

Quasar integrates a SIMD-optimized aggregation engine with distributed execution to query both live and historical data seamlessly. The system operates as a single logical database, eliminating the need for separate real-time, warehouse, and data lake layers or continuous tuning.

  • No separation between streaming and batch
  • No pipeline stitching
  • Predictable performance regardless of data age

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