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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

github.com/meltano/meltano · homepage · Python · MIT (permissive) 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

Full methodology

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

Member repositories

RepositoryRoleHealth v2
meltano/meltanomain97

For agents

markdown · JSON · MCP: product_card(name="meltano/meltano")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem