meltano/meltano
Meltano: the declarative code-first data integration engine that powers your wildest data and ML-powered product ideas. Say goodbye to writing, maintaining, and scaling your own API integrations. observed · 2026-08-28
Health v2 · maintenance only
97/100
- Activity 99
- Release rhythm 94
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: 8.0
- age_days: 1899
- days_rel: 42
- days_push: 7
- n_releases_24m: 37
Adoption not part of the score
2610 stars · 263 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Meltano is an open-source, declarative, code-first data integration engine and CLI for building and running ELT pipelines. It orchestrates 600+ pre-built Singer-based extractors (taps) and loaders (targets) to move data from SaaS apps, APIs, files, and databases into warehouses, lakes, and vector stores, with built-in support for environments, scheduling, and dbt transformations.
Use cases
- extract and load data from SaaS APIs and databases into a data warehouse
- build declarative ELT pipelines as code with meltano.yml
- self-host a data integration stack with 600+ connectors
- schedule and orchestrate recurring data pipelines with environments for dev and prod
- run dbt transformations alongside extract and load jobs
- build custom Singer taps and targets with the Meltano SDK
- replace expensive row-based ETL tools like Fivetran with a self-managed alternative
When to choose
- you want a code-first, Git-managed alternative to Fivetran or Airbyte with no per-row pricing
- your team has DevOps capacity and wants full control self-hosting data pipelines
- you need to connect many SaaS tools, REST APIs, and databases using the Singer connector ecosystem
- you want pipeline configuration, environments, and deployment managed declaratively in one project
- you need to integrate dbt transformations and testing into the same ELT workflow
When to avoid
- you need a fully managed service with zero infrastructure to maintain and no in-house DevOps
- your source or destination has no existing Singer tap or target and you cannot build one
- you only need a one-off data sync without ongoing pipeline management
- you require a GUI-first experience rather than a CLI-driven workflow
Facets
framework · maturity stable
etl cli workflow-automation scheduling configuration-management developer-tools data-science analytics big-data self-hosted developer-tools python cli self-hosted cross-platform elt dataops singer taps targets connectors data-pipelines extract-load dbt declarative-configuration meltano-hub meltano-sdk data-integration pipeline-orchestration code-first open-source data-engineering automation docker
7 sources
- readme: https://github.com/meltano/meltano · fetched 2026-08-28 · df1afbb03d29
- homepage: https://meltano.com/ · fetched 2026-08-29 · d295f0e64afe
- site_page: https://docs.meltano.com/ · fetched 2026-08-29 · 749442307f3f
- site_page: https://meltano.com/about · fetched 2026-08-29 · c768ade5fa36
- registry_pypi: https://pypi.org/pypi/meltano/json · fetched 2026-08-29 · 7f47e4d97f8d
- site_page: https://meltano.com/pricing · fetched 2026-08-29 · 270c118c8a99
- site_page: https://meltano.com/pricingcalculator · fetched 2026-08-29 · dad6f6a1ff6d
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| meltano/meltano | main | 97 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem