Airbyte
Open-source data integration engine for ELT pipelines across data sources
Meltano
Declarative code-first data integration engine for modern pipelines
Meltano is a Python-based data integration engine that connects 600+ APIs and databases through declarative configuration, eliminating custom integration code.
Overview
Modern Data Integration, Simplified
Meltano is a declarative, code-first data integration engine built for data engineers and teams who need reliable, scalable pipelines without the overhead of custom API integrations. Built on Python and leveraging the Singer ecosystem, it provides access to 600+ pre-built connectors for APIs and databases through simple YAML configuration.
Built for DataOps Teams
Designed with version control and CI/CD workflows in mind, Meltano treats data pipelines as code. Teams can define, test, and deploy ELT workflows using declarative configuration files, making pipelines reproducible and maintainable. The platform integrates seamlessly with modern data stacks and supports containerized deployments through optimized Docker images.
Community-Powered Ecosystem
Meltano Hub serves as the central registry for discovering and sharing plugins, taps, and targets. With an active community of 2,500+ data professionals and MIT licensing, the project benefits from continuous contributions and a growing connector ecosystem. Whether you're building ML pipelines or consolidating data from multiple sources, Meltano provides the foundation for scalable data operations.
Highlights
600+ pre-built connectors for APIs and databases via Meltano Hub
Declarative YAML configuration for version-controlled pipelines
Singer tap and target ecosystem integration
Optimized Docker images with cloud storage and database support
Pros
- Eliminates need to write and maintain custom API integration code
- Code-first approach enables GitOps workflows and CI/CD integration
- Large connector ecosystem with community-contributed plugins
- MIT license provides flexibility for commercial and private use
Considerations
- Requires familiarity with Python ecosystem and command-line tools
- Learning curve for declarative configuration patterns
- Dependent on Singer tap/target quality and maintenance
- May require custom development for unsupported data sources
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Fit guide
Great for
- Data engineering teams adopting DataOps and GitOps practices
- Organizations needing reproducible, version-controlled ELT pipelines
- Teams consolidating data from multiple APIs and databases
- Projects requiring containerized, cloud-native data workflows
Not ideal when
- Non-technical users seeking no-code visual pipeline builders
- Teams without Python or containerization experience
- Projects requiring real-time streaming data integration
- Organizations needing enterprise GUI-based management tools
How teams use it
Multi-Source Data Consolidation
Centralize data from 600+ APIs and databases into a data warehouse using declarative configuration without custom integration code.
ML Pipeline Data Preparation
Build reproducible ELT workflows that extract, transform, and load training data from multiple sources for machine learning models.
GitOps-Driven Analytics Infrastructure
Version control data pipelines as code, enabling peer review, automated testing, and CI/CD deployment of data workflows.
Cloud-Native Data Operations
Deploy containerized data pipelines with optimized Docker images supporting cloud storage and multiple database connectors.
Tech snapshot
Python99%
Shell1%
Dockerfile1%
Slim1%
Mako1%
Frequently asked questions
What is the difference between Meltano and traditional ETL tools?
Meltano uses a declarative, code-first approach where pipelines are defined in version-controlled YAML files rather than GUI-based configurations, enabling GitOps workflows and CI/CD integration.
How many data sources does Meltano support?
Meltano provides access to 600+ APIs and databases through its Hub, which aggregates Singer taps, targets, and community-contributed plugins.
Do I need to know Python to use Meltano?
While Meltano is built in Python, basic usage requires only YAML configuration. Python knowledge is helpful for custom plugin development or advanced transformations.
What Docker images are available?
Meltano offers slim images (recommended, with cloud storage support) and full images (including all database connectors and build tools) on Docker Hub.
Can I add custom connectors to Meltano?
Yes, users can create and contribute plugins to Meltano Hub, making them immediately discoverable and usable by the community.
Project at a glance
Active- Stars
- 2,323
- Watchers
- 2,323
- Forks
- 193
LicenseMIT
Repo age4 years old
Last commit2 days ago
Primary languagePython
Last synced 3 hours ago