Apache Arrow DataFusion vs Firebolt

Apache Arrow DataFusion is a free, extensible query engine written in Rust that uses Apache Arrow as its in-memory format. It is intended for developers building database and analytics systems tailored to particular workloads, rather than as a ready-made hosted database. Queries run in-process, using threads for parallel execution. DataFusion offers SQL and DataFrame APIs, while related subprojects provide Python and Java interfaces. Documented capabilities include SQL parsing and planning, parallel and streaming execution, and query optimization. Built-in data sources include CSV, Parquet, JSON, Avro, and Arrow. Developers can add sources through the TableProvider trait and customize functions, operators, and other components. Runtime features include enforced memory limits and disk spilling for sorts, grouping, and joins. The core project is designed for in-process use; Ballista is a related distributed processing extension. Rust users commonly add DataFusion from crates.io, while official Apache releases are source artifacts. The project recommends verifying release artifacts with an OpenPGP signature or SHA-512 checksum.

Firebolt is an open-source analytical database for engineers, with Postgres-compatible SQL and options for self-managed or managed deployment. It can run as a single binary on a laptop or scale to a cluster of hundreds of nodes in a customer’s cloud. Firebolt is designed for sub-second query latency and high-concurrency analytics, and supports Apache Iceberg and DuckLake using open storage standards. Deployment choices include a single binary, Helm chart, Kubernetes Operator, and managed service. Firebolt Core is free forever for self-hosted use, with no usage limits; it cannot be used to build a hosted SaaS that competes with Firebolt’s managed service. Managed compute is billed per second, with scale-to-zero, automatic start and stop, and autoscaling. Object storage is pass-through at $0.0264 / GB·mo, and the managed service includes $200 free credits. AI capabilities include query and embedding functions, vector search, an MCP Server, and a preview agent. Self-managed availability includes AWS and GCP, with Azure in preview; the managed service is currently AWS-only.