Meltano: Dependable Data by Default

3 min read Original article ↗

Dependable data by
default

Meltano is an ETL platform built on open-source foundations,
engineered by experts for cloud scale, efficiency, and reliability.

Reduce
cost

Pay for the workloads you actually run. Costs stay predictable as data volumes grow, whether you self-manage Meltano or use a managed orchestrator.

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Increase
efficiency

Build, adjust, and debug connectors directly. You are not blocked by ticket queues, and support is available when you need it, not as a gatekeeper.

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Centralize
movement

Run all pipelines in one place, across databases, files, SaaS tools, internal systems, and workflows like dbt.

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Remove
constraints

Add new sources, apply transformations before the warehouse, adjust connectors, and let teams contribute pipelines without artificial limits.

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MELTANO
Model

Meltano Models your data stack as code and provides built-in release management and governance using Git and GitHub workflows.
  • Implement controlled CI/CD pipelines for developing, testing, and promoting pipeline changes with full traceability and auditability.
  • Enforce strong governance and security with isolated customer environments, secure credential storage and encrypted data transfer.
  • Named Meltano environments, local development environments and deployment management into development and production workspaces are just some of the ways we help you make your data reliable by default. 
environments:
 - name: prod
   env:
       MY_ENV_VAR: $MELTANO_PROJECT_ROOT/prod/file.json
 - name: dev
   env:
       MY_ENV_VAR: $MELTANO_PROJECT_ROOT/dev/file.json

MELTANO
Extract and Load

Meltano supports 600+ pre-built connectors, making it straightforward for data teams to extract and load data via the UI, CLI, API, or AI.
  • Connect and ingest data from SaaS applications, REST APIs, semi-structured data (JSON and XML), and relational databases.
  • Choose your replication strategy: full, incremental, or log-based. Batch or near real-time. Configured in code.
  • Built-in idempotency keeps your pipelines consistent and self-correcting. Backfills are simple. Duplicates are handled automatically.
meltano add tap-postgres
meltano add target-snowflake
meltano config set tap-postgres sqlalchemy_url postgresql://warehouse:warehouse@localhost:5432/warehouse
meltano run tap-postgres target-snowflake

MELTANO
Transform

Meltano integrates directly with dbt™ for transformation and Elementary for data validation, both configured and version-controlled inside your Meltano project.
  • Manage your data models in version control. Every change is tracked, reviewable, and reversible.
  • Build and test models in plain SQL using your IDE or an AI assistant. No proprietary language, no lock-in.
  • Data quality checks run automatically. Schema drift, completeness gaps, and anomalous values are caught before they reach your analysts.
meltano add --plugin-type transformer dbt
claude --inline “Add a job model with data quality tests to this meltano dbt project”
meltano invoke dbt show job
meltano invoke dbt run test

MEL<em>T</em>ANO<br>Transform

MELTANO
Analytics and Notebooks

Meltano helps data teams prepare dependable data for their Analytics and Notebooks.
  • Omni, Power BI, Looker, Sigma, or any other BI Tool
  • Cube, dbt™, Snowflake, Preset or any other Semantic Layer
  • Built-in support for running Notebooks
meltano add extractor tap-postgres
meltano add loader target-snowflake
cookiecutter https://github.com/meltano/sdk \
--directory="cookiecutter/tap-template
meltano add --custom extractor tap-my-api
meltano add utility dbt-snowflake
meltano add --custom utility my-script.py
meltano run tap-postgres target-snowflake dbt-snowflake
meltano run tap-my-api target-snowflake my-script.py

MELTANO
Orchestrate

Meltano Cloud provides robust and built-in orchestration for data operations with dozens of pipelines. Teams with hundreds of pipelines can use Meltano for Extract and Load and Apache Apache Airflow®, Dagster or Orchestra.
  • Monitor system health, ingestion job status, and pipeline performance with integrated monitoring, logging, and alerting.
  • Schedule and automate pipelines to match business cadence and support end-to-end data workflows.
  • Quickly troubleshoot and recover from issues with detailed logs, diagnostics, and the ability to perform full or partial re-syncs to correct data inconsistencies.
plugins:
 extractors:
 - name: tap-postgres
   config:
     host: pg.example.com
     user: admin
     password: $PG_PASSWORD
   select:
     - users.*
 loaders:
 - name: target-snowflake
schedules:
- name: app-to-dw
 interval: @hourly
 extractor: tap-postgres
 loader: target-snowflake

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