faucet-stream vs. Redpanda Connect (Benthos)
The other declarative-YAML single binary. Here’s where each one wins — including an honest note on the license history.
Reflects each tool as of 2026-07. Verify licensing against the current project
LICENSEfiles, which are the source of truth per component.
The short version
Redpanda Connect is the tool formerly known as Benthos (acquired by Redpanda in 2024) — a Go stream processor configured with declarative YAML (input → processors → output). It’s streaming-first, has a large component library, and is the closest architectural analogue to faucet-stream’s config-driven model. Ships as a single binary or as managed pipelines on Redpanda Cloud.
faucet-stream is built for batch/ELT data movement rather than continuous stream processing: incremental + resumable replication, snapshot→CDC, first-class warehouse sinks, and governance in the movement path — under uniform permissive licensing.
Reach for Redpanda Connect for continuous, record-by-record streaming; reach for faucet-stream to move data between databases, object stores, and warehouses as discrete, resumable, governed runs.
A note on licensing
Worth stating precisely, because it’s a real adoption consideration: Benthos was originally MIT. After the Redpanda acquisition the maintained repo moved to a mix of Apache-2.0 and a source-available Redpanda Enterprise/Community license, with some components (certain CDC inputs and others) gated behind the enterprise license. The community forked the pre-relicensing project as Bento, which continues under permissive terms. faucet-stream is uniformly MIT / Apache-2.0 with no enterprise-gated connectors.
Where faucet-stream is different
- Batch/ELT is the home turf. Incremental + resumable replication, snapshot→CDC handoff, and first-class warehouse sinks (BigQuery, Snowflake, Iceberg, Delta) — the job faucet is built for.
- Governance in the movement path. Data-quality checks, versioned data contracts, PII masking (before any sink sees a row), schema-drift policy, column-level lineage (OpenLineage) + a catalog, and freshness/volume SLAs — native and zero-config.
- Effectively-once delivery. Per-page commit tokens commit atomically with the data, so a resumed run drops duplicates — across 11 sinks (SQL, Kafka, Iceberg, BigQuery, Snowflake, Spanner, MongoDB, Redis).
- Uniform permissive licensing. MIT / Apache-2.0 throughout — no per-component enterprise gate to audit.
Where Redpanda Connect is the better choice
Straight with you — for its core job it’s excellent:
- Continuous, record-by-record streaming. It’s purpose-built for never-ending stream processing with a rich processor/transform library. faucet runs discrete pipelines to completion — even its long-running modes (
faucet schedule,faucet serve) orchestrate complete runs, not an endless stream. - Deep Redpanda/Kafka ecosystem integration and a large, mature component catalog.
- Battle-tested across years of production stream-processing use, with a Go library you can embed.
Side-by-side
| faucet-stream | Redpanda Connect (Benthos) | |
|---|---|---|
| Language / runtime | Rust, single binary | Go, single binary |
| Orientation | discrete runs to completion | continuous stream processing |
| Connectors | 66 source/sink, ETL/CDC/warehouse | hundreds of components/processors |
| Change data capture | ✓ engine-level | some CDC inputs (several enterprise-gated) |
| Warehouse / ELT sinks | ✓ BigQuery, Snowflake, Iceberg, Delta, … | more messaging/stream-oriented |
| Governance in-path (quality / contracts / masking / lineage / SLA) | ✓ native | ✗ |
| Effectively-once delivery | ✓ (11 sinks incl. Kafka, Iceberg, BigQuery) | ✗ |
| Embeddable as a library | ✓ (Rust) | ✓ (Go) |
| License | MIT / Apache-2.0 | Apache-2.0 + source-available enterprise |
Rule of thumb
If the workload is a continuous stream you transform in flight, Redpanda Connect is purpose-built for it. If the workload is moving data between APIs, databases, object stores, and warehouses as discrete, resumable, governed runs — see replication (snapshot → CDC) — that’s faucet-stream.
See for yourself
- Choosing a connector — confirm your sources and sinks are covered.
- Try it in 60 seconds — no infrastructure needed.
- Benchmarks — full methodology and honest caveats.