DBiggerN

DBiggerN is a distributed relational database system designed for high scalability and low latency. It combines a sharded storage layer with a highly tunable query engine, enabling enterprises to handle petabyte‑sized workloads while maintaining sub‑millisecond read and write performance.

The project was founded by a team of former engineers from several open‑source database initiatives. In 2019 the first open‑source release appeared on GitHub, followed by a commercial edition aimed at data‑centric organizations in finance, telecommunications and e‑commerce. DBiggerN’s development has been steered by an active community of contributors and an advisory board of industry experts.

Architecturally, DBiggerN employs a masterless consensus protocol for replication, leveraging the Raft algorithm to achieve strong consistency across nodes. Data is partitioned by range and distributed across shards, each containing a read‑optimized digest and a write‑optimized log. The query planner automatically coalesces local statistics to generate efficient execution plans, and the optimizer supports full outer joins, lateral subqueries and recursive CTEs. The system also integrates a lightweight, columnar storage engine to accelerate analytical queries without sacrificing transactional performance.

Since 2020, several Fortune 500 companies have adopted DBiggerN in mission‑critical environments, citing predictable performance and rapid scaling as primary benefits. Benchmarking studies have shown that DBiggerN can outperform legacy systems by up to 70 % on mixed workloads in clustered deployments. The product has also been featured in major conference presentations and research articles exploring distributed consistency models.

Looking ahead, the roadmap for DBiggerN includes native support for serverless deployment on major cloud platforms, an enhanced machine‑learning metadata layer, and continuous integration of multi‑tenant isolation mechanisms to enable SaaS offerings. The ongoing evolution aims to cement DBiggerN’s role as a key platform for next‑generation, data‑heavy applications.