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faucetstream

faucet-stream vs. Meltano (Singer)

Running Meltano today, or evaluating it? Here’s an honest, specific comparison — no strawmen.

The short version

Section titled “The short version”

Meltano is the most popular open-source runtime for the Singer spec — a mature, Python-based EL(T) platform with a 600+ tap ecosystem and a large community. If tap breadth is your first requirement, Meltano is hard to beat.

faucet-stream makes a different bet: one native Rust binary (or an embeddable library), roughly an order of magnitude faster, with data governance built into the movement path — no Python environment to manage, no plugins to assemble for quality, contracts, masking, or lineage.

Move to faucet-stream when throughput, operational simplicity, or in-flight governance matter more than raw connector count.

Where faucet-stream is different

Section titled “Where faucet-stream is different”
  • Speed you can measure. On a reproducible 1M-row CSV→JSONL move, faucet does 701k rows/s in 20 MiB vs Meltano’s 8.7k rows/s in 819 MiB — ~80× faster, ~40× less memory, with the same row count. Sink-bound moves (e.g. Postgres→Postgres) narrow the gap — the benchmarks show that scenario too, honestly. The difference is structural: no per-row Python overhead, native streaming with bounded memory.
  • No Python runtime. faucet is a single static binary — brew install, drop it on a box, done. No virtualenv, no plugin resolution, no Python-version matrix to keep green in CI and prod.
  • Governance in the movement path, not bolted on. Data-quality checks, versioned data contracts, PII masking (applied before any sink sees a row), schema-drift policy, column-level lineage (OpenLineage) + a data-movement catalog, and freshness/volume SLAs are native and zero-config. In the Singer world these are separate concerns you assemble (mappers, dbt tests, external tooling).
  • Effectively-once delivery. Per-page commit tokens commit atomically with the data, so a resumed run drops duplicates — across 13 sinks (SQL, Oracle, Kafka, Iceberg, BigQuery, Snowflake, Spanner, Databricks, MongoDB, Redis), plus a keyed-upsert path on any source into an upsert-capable sink.
  • Embeddable. Compile the same engine into your own Rust service via the typed Source / Sink traits — not just a CLI.

Where Meltano is the better choice

Section titled “Where Meltano is the better choice”

Straight with you, because it’s what makes the rest credible:

  • Connector breadth. 600+ Singer taps vs faucet’s 58 built-in connectors. Need a long-tail SaaS source today? Meltano (or a Singer tap) probably already has it.
  • A mature ecosystem & community. Years of taps, docs, Meltano Hub, and an active community. faucet is younger.
  • You’re already invested in Singer/dbt. If your stack is Singer taps + dbt and it’s working, switching only pays off where the wins above are things you actually feel.

Side-by-side

Section titled “Side-by-side”
faucet-stream Meltano (Singer)
Runtime Rust, single native binary Python
Install one binary / brew / cargo Python env + plugins
Connectors 78 (43 sources, 35 sinks), growing 600+ taps
Throughput (1M-row CSV→JSONL) 701k rows/s, 20 MiB 8.7k rows/s, 819 MiB
In-flight transforms ✓ 11 record transforms + filter/explode/CDC-unwrap + embedded-DuckDB sql mappers; dbt post-load
Data quality / contracts / masking ✓ native, in-path assemble (mappers, dbt tests)
Lineage + catalog ✓ OpenLineage, native external
Effectively-once delivery ✓ (13 sinks incl. Kafka, Iceberg, BigQuery, Databricks, Oracle) ✗
Embeddable as a library ✓ (Rust) ✗
License MIT / Apache-2.0 MIT

Migrating from Meltano

Section titled “Migrating from Meltano”

The mental model maps cleanly:

Meltano / Singer faucet-stream
extractor (tap) a source
loader (target) a sink
meltano.yml a faucet.yaml pipeline: block
Singer STATE a resumable state: bookmark
stream maps / mappers transforms: (incl. the sql transform)

Keep a Singer target you depend on

Section titled “Keep a Singer target you depend on”

You do not have to replace both sides at once. The singer sink runs any existing Singer target and feeds it faucet records, so a pipeline can move one side at a time: first swap the tap for a native faucet source and keep the target, then swap the target when a native sink covers it (or run a tap through the singer source into a native sink — the mirror image).

# was: meltano run tap-csv target-jsonl
version: 1
name: orders
pipeline:
  source:
    type: file                      # native faucet source replaces the tap
    config: { path: ./data/orders.csv }
  sink:
    type: singer                    # the Singer target you already run
    config:
      target_command: target-jsonl
      target_config:                # what used to be the loader's config in meltano.yml
        destination_path: ./out

What carries over and what changes:

Meltano loader setting faucet singer sink
pip_url / executable install the target yourself; target_command is its path or PATH name
loader config: target_config: — written to a private (0600) temp file passed as --config; its values are scrubbed from the target’s stderr in faucet’s logs and errors
environment variables env: — on top of faucet’s own environment, which inherit_env: false (or a list of names) narrows to a baseline
stream name the matrix row id (or the pipeline name for a single-row config); override with stream:
key_properties from the tap write_mode: upsert + key: [...] (or key_properties:)
full-table / ACTIVATE_VERSION write_mode: overwrite — records carry the run’s version and ACTIVATE_VERSION is sent only after a successful run
Meltano’s state backend faucet’s state: block — a bookmark is saved only after the target confirms the records before it

Confirmation is flush_on: exit by default: at every flush the target’s input is closed and faucet waits for a clean exit, which works with every target (most, including Meltano SDK targets, only emit STATE at end of input). For a target that echoes STATE as soon as it has persisted the preceding records, flush_on: state keeps one target running and waits for the echo. A target that exits non-zero fails the run with its last stderr lines. See the faucet-sink-singer README and the example cli/examples/csv_to_singer_target.yaml.

For the whole procedure (inventory, carrying bookmarks over, a verified parallel run and cutover), see Migrating from Meltano, Singer or Airbyte; for a step-by-step walkthrough with before/after configs, the Migrating from Meltano/Singer guide. Then start from your first pipeline and the connector catalog.

See for yourself

Section titled “See for yourself”