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Cloud-native data warehouse with compute-storage separation for large-scale analytics
ByConity is a cloud-native data warehouse derived from ClickHouse, featuring compute-storage separation, advanced query optimization, and unified batch and streaming data ingestion for high-performance analytics.
ByConity is an advanced database management system built on ClickHouse v21.8 foundations, reimagined with compute-storage separation inspired by Snowflake's architecture. Designed for organizations managing large-scale analytical workloads, it delivers high-performance querying capabilities while breaking down data silos through unified batch and streaming data ingestion.
The platform introduces stateless workers, a sophisticated query optimizer, and a shared-storage framework that enables independent scaling of compute and storage resources. This architecture allows teams to extract insights from vast datasets quickly and accurately, without maintaining separate processes for different data ingestion patterns.
ByConity's cloud-native design works seamlessly on both Kubernetes and physical clusters, offering deployment flexibility to match your infrastructure requirements. Built in C++ and released under Apache 2.0 license, it provides enterprise-grade performance while maintaining the extensibility and community collaboration benefits of open-source software. The system requires FoundationDB client libraries and can be deployed efficiently using Docker Compose for cluster orchestration.
When teams consider ByConity, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Real-Time Analytics on Streaming Data
Ingest and query streaming events alongside historical batch data without maintaining separate systems, enabling unified analytics across all data sources.
Elastic Cloud Data Warehouse
Scale compute resources independently during peak query loads while maintaining cost-efficient storage, optimizing infrastructure spend for variable workloads.
Breaking Down Enterprise Data Silos
Consolidate isolated batch and streaming data sources into a single queryable platform, improving cross-functional insights and reducing operational complexity.
Large-Scale Log Analytics
Query petabyte-scale log datasets with sub-second response times using advanced query optimization and distributed compute architecture.
ByConity builds on ClickHouse v21.8 but introduces compute-storage separation, an advanced query optimizer, stateless workers, and shared-storage architecture inspired by Snowflake. These architectural changes are substantial enough that integration into upstream ClickHouse was not feasible.
ByConity requires the FoundationDB client library (libfdb_c.so) to run. It can be deployed on Kubernetes clusters or physical machines, with Docker Compose recommended for convenient cluster deployment.
Yes, ByConity seamlessly ingests both batch-loaded data and streaming data, eliminating the need for separate processing pipelines and helping break down data silos for unified analytics.
Yes, ByConity is designed with a cloud-native approach featuring compute-storage separation, stateless workers, and support for Kubernetes deployments, allowing it to leverage cloud scalability and resilience while also supporting physical clusters.
ByConity is released under the Apache 2.0 license, making it open source and available for community collaboration, contribution, and customization.
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