Open Source Series: What is Meltano?

The DevOps-First ELT Framework for Data Engineers

As the open-source data tooling space matures, more teams are turning to frameworks that offer flexibility without vendor lock-in. Meltano is one of the leading open-source ELT tools designed specifically for data engineers who want to treat their pipelines like production code—modular, testable, version-controlled, and CI/CD-ready.

This post introduces what Meltano is, how it works, and who it's best suited for—without diving into self-hosting or deployment just yet.

What is Meltano?

At its core, Meltano is an open-source ELT framework built to give developers full control over their data pipelines. It wraps proven tools—Singer for extraction, dbt for transformation, and more—into a code-first experience that prioritizes local development and DevOps best practices.

Unlike tools like Airbyte that emphasize UI-first interaction, Meltano assumes you’ll live in the terminal, collaborate via Git, and manage environments like a software project.

TL;DR: If you’ve ever wanted to treat your data stack like an app—with CI/CD, staging environments, and modular configs—Meltano was built for you.

Core Concepts

To understand Meltano, it helps to get familiar with its building blocks:

Concept	Description
Pipelines as Code	Pipelines are configured in a `meltano.yml` file, stored in your repo, and versioned like any other codebase.
Plugin Architecture	Everything—extractors, loaders, transformers, utilities—is a plugin that can be added or swapped with a single command.
Environments	Easily define dev, staging, and prod environments with overrides.
CLI-First Interface	The Meltano CLI powers everything from initializing projects to running pipelines and scheduling jobs.
Modular by Design	Use Meltano as a framework to stitch together the tools you want to use—not just what comes in the box.

A Simple Example

A typical Meltano workflow might look like this:‍

meltano init my_project
cd my_project

meltano add extractor tap-postgres
meltano add loader target-snowflake
meltano add transformer dbt

meltano run tap-postgres target-snowflake dbt:run

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In a few commands, you’ve created a full ELT pipeline using open standards—all defined in code, and ready for CI/CD.

Supported Integrations

Thanks to its adoption of the Singer specification, Meltano offers compatibility with a wide range of data sources and destinations:

Component	Description
Extractors (Taps)	MySQL, Stripe, Salesforce, Shopify, etc.
Loaders (Targets)	Snowflake, Redshift, BigQuery, Postgres
Transformers	Full dbt support out of the box
Utilities	Integrate Airflow, Great Expectations, Superset

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Need something custom? Meltano maintains the Singer SDK to help you build and maintain your own connectors.

Who is Meltano For?

Meltano isn't trying to be everything to everyone. Its ideal audience includes:

Data engineers who prefer Git over GUIs
Teams running production pipelines and want robust local dev + deployment workflows
Shops with strong DevOps culture who treat data pipelines like software releases
Builders who want to integrate or extend open standards, not be locked into one platform

Pros and Considerations

Why choose Meltano?

Pros	Considerations
Code-first, version-controlled ELT	No built-in GUI
Open-source and highly extensible	Steeper learning curve for non-engineers
Dev-friendly CLI and plugin ecosystem	Quality of Singer connectors can vary; maintenance is community-driven
Full support for dbt, Great Expectations, Airflow	Assumes technical ownership of pipelines and infrastructure

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Meltano’s flexibility is powerful—but it assumes your team is ready to manage that power.

Community and Evolution

Meltano began as an internal project at GitLab and is now backed by a growing open-source community. It’s under active development with:

A vibrant Slack workspace
A rich plugin hub
An SDK for building custom connectors
A roadmap focused on developer experience, orchestration, and plugin maintainability

Conclusion

Meltano is a great choice for teams that want to own their ELT stack—fully, transparently, and with engineering-grade rigour.

If you’re already version-controlling your dbt models, writing Airflow DAGs, or managing environments through code, you’ll find Meltano fits naturally into your workflow.

In upcoming posts, we’ll explore how Meltano compares to Airbyte and dlt Hub, and later, we’ll walk through how to self-host and productionize ELT pipelines as part of our Open Source Series.

Until then, you can try it out at meltano.com or browse the code on GitHub.