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meltano avatar

meltano/meltano

0
View on GitHub↗
2,534 stars·245 forks·Python·MIT·37 viewsmeltano.com↗

Meltano

Meltano is an open-source platform for building, running, and orchestrating ELT (Extract, Load, Transform) data pipelines. It provides a declarative, YAML-driven configuration system that defines entire pipeline workflows, including data connectors, schedules, and transformations, without requiring imperative code. The platform is built on the Singer specification for data connectors and integrates with dbt for SQL-based transformations and Apache Airflow for scheduling and orchestration.

What distinguishes Meltano is its comprehensive approach to pipeline management, combining a curated catalog of over 600 pre-built connectors with the ability to build custom ones using a dedicated plugin SDK. The platform supports multiple deployment models, including self-hosted infrastructure, Docker containers, and a managed cloud service that offloads execution and monitoring. It also features workspace isolation for separating projects and teams, vault-based secret injection for secure credential management, and integration with external monitoring tools for operational visibility.

The platform covers the full lifecycle of data integration, from discovering and installing connectors to configuring replication methods and selecting specific tables and columns for extraction. It supports running end-to-end pipelines that combine extraction, loading, and transformation in a single workflow, with the ability to schedule recurring runs and automate complex dependencies. For organizations needing custom data sources or destinations, Meltano provides a software development kit for building and registering new connectors that become immediately usable within the platform.

Documentation and installation options include self-hosted deployment guides, Docker Compose configurations, and GitLab CI/CD templates for automated image building.

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Features

  • Business Intelligence - Data lifecycle management for loading, transforming, and analyzing business data.
  • Data Integration - Declarative, code-first engine for managing data integration pipelines.
  • Data Pipelines and Orchestration - Platform for managing the end-to-end data lifecycle.

Star history

Star history chart for meltano/meltanoStar history chart for meltano/meltano

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does meltano/meltano do?

Meltano is an open-source platform for building, running, and orchestrating ELT (Extract, Load, Transform) data pipelines. It provides a declarative, YAML-driven configuration system that defines entire pipeline workflows, including data connectors, schedules, and transformations, without requiring imperative code. The platform is built on the Singer specification for data connectors and integrates with dbt for SQL-based transformations and Apache Airflow for scheduling and…

What are the main features of meltano/meltano?

The main features of meltano/meltano are: Business Intelligence, Data Integration, Data Pipelines and Orchestration.

Which projects share features with meltano/meltano?

Projects with overlapping indexed features include: airbytehq/airbyte — Airbyte is a data integration platform designed to synchronize information between diverse applications, databases,… singer-io/getting-started — Singer is an open source standard for moving data between databases, web APIs, files, queues, and just about anything… jitsucom/jitsu — Jitsu is an open-source customer data platform designed to orchestrate event data pipelines. It captures, transforms,… astronomer/dag-factory — Dag-factory is a framework for constructing and managing Apache Airflow data pipelines through declarative… apache/superset — Superset is a web-based business intelligence platform designed for data exploration, visualization, and interactive… apache/inlong — Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file…

Projects sharing features with Meltano

These projects share indexed features with Meltano. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • singer-io/getting-startedsinger-io avatar

    singer-io/getting-started

    1,342View on GitHub↗

    Singer is an open source standard for moving data between databases, web APIs, files, queues, and just about anything else you can think of. The Singer spec describes how data extraction scripts — called “Taps” — and data loading scripts — called “Targets” — should communicate using a standard…

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    View on GitHub↗1,342
  • airbytehq/airbyteairbytehq avatar

    airbytehq/airbyte

    21,472View on GitHub↗

    Airbyte is a data integration platform designed to synchronize information between diverse applications, databases, and data warehouses. It functions as an extract, transform, and load orchestrator that manages automated data movement workflows across cloud, on-premise, and hybrid environments. The platform provides a standardized interface for connectors, enabling the movement of structured and unstructured data while maintaining stateful checkpoints for reliable incremental syncing. The platform distinguishes itself through a containerized architecture that isolates connectors to prevent de

    Pythonbigquerychange-data-capturedata
    View on GitHub↗21,472
  • jitsucom/jitsujitsucom avatar

    jitsucom/jitsu

    4,782View on GitHub↗

    Jitsu is an open-source customer data platform designed to orchestrate event data pipelines. It captures, transforms, and routes behavioral data from web and server sources into data warehouses and analytics tools, providing a unified infrastructure for managing event streams. The platform distinguishes itself through its focus on self-hosted, containerized operations that grant users full control over their data security and privacy. It features a robust identity resolution engine that stitches disparate user identifiers into persistent profiles across sessions and devices, alongside program

    TypeScriptbigqueryclickhousedata-collection
    View on GitHub↗4,782
  • astronomer/dag-factoryastronomer avatar

    astronomer/dag-factory

    1,440View on GitHub↗

    Dag-factory is a framework for constructing and managing Apache Airflow data pipelines through declarative configuration files. By replacing manual procedural code with structured YAML definitions, it enables the programmatic generation of complex workflow structures, task dependencies, and execution schedules. The project distinguishes itself by mapping configuration keys directly to Python class constructors and operators, allowing for the dynamic instantiation of objects and custom logic. It supports hierarchical configuration inheritance to standardize settings across environments and pro

    Pythonairflowapache-airflowdags
    View on GitHub↗1,440
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