Meltano is an open-source ELT platform designed for data engineers, facilitating data extraction, loading, and transformation using Singer taps and targets.
Why it matters
overview
Meltano is an open-source ELT (extract, load, transform) data integration engine developed by Meltano (company) that enables data engineers to build and manage their entire data stack as code. It originated within GitLab's internal data team and spun out as an independent company in June 2021, emphasizing a code-first, Git-native approach to managing data pipelines.
features
Meltano provides a comprehensive set of features for data engineers to manage ELT workflows, focusing on extensibility and a code-first approach.
use cases
Meltano is primarily designed for data engineers and data teams who prefer a code-first, Git-native approach to data integration and pipeline management.
how to use
Meltano is primarily interacted with via its command-line interface, allowing users to define and manage data pipelines declaratively.
pip install meltano.meltano init <project_name>.meltano add extractor <extractor_name>.meltano add loader <loader_name>.meltano config <plugin_name> set <setting> <value>.meltano run <extractor_name> <loader_name>.pricing
Meltano's core platform is open-source and free to use under the Apache 2.0 license. This includes access to its extensive connector library and development SDKs. Meltano Cloud offers additional managed services and features, including an AI Data Engineer agent, and passed its SOC2 Type 1 Audit in September 2023.
Pricing Page
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Meltano differentiates itself from other data integration and orchestration tools through its strong emphasis on a code-first, Git-native approach and its deep integration with the Singer specification.
Offers a user interface in addition to an API and CLI for managing data connectors and pipelines, and supports a vast library of connectors.
Airbyte provides a more comprehensive, often GUI-driven experience for managing connectors and syncs, whereas Meltano is more CLI-centric and focused on integrating Singer taps within a data engineering workflow. While both are open-source ELT, Airbyte often feels more like a standalone product for data movement.
Focuses exclusively on the 'T' (transformation) aspect of ELT, allowing data engineers to define transformations as SQL models.
dbt Core handles only the transformation layer, meaning you'll need separate tools (like Singer taps or custom scripts) for extraction and loading, which Meltano aims to provide as a unified framework. Meltano covers E, L, and orchestrates T, while dbt is purely T.
A Python module for building complex pipelines of batch jobs, focusing on task dependencies, workflow management, and fault tolerance.
Luigi is a general-purpose workflow orchestrator that requires you to build your ELT tasks (including E and L using tools like Singer taps) from scratch, whereas Meltano provides a more opinionated framework specifically for ELT with built-in Singer integration. Luigi gives you more low-level control but less out-of-the-box ELT functionality.
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