
IBM StreamSets
Real-time streaming data pipelines that enhance real-time decision-making and mitigate risks.

CLI-first, open source ELT for limitless creativity.

Meltano is an open-source, CLI-first ELT (Extract, Load, Transform) tool designed to provide data engineers with control and visibility over their data pipelines. It allows users to extract data from various sources (SaaS APIs, databases, files), load it into destinations like data warehouses and data lakes, and transform it using a variety of tools. Meltano emphasizes a code-first approach, enabling version control and customization of ELT processes. It supports a wide range of data sources and destinations, aiming to cover 100% of users' data integration needs, including niche or internal systems. By offering a declarative, code-first engine, Meltano seeks to eliminate the 'black box' nature of traditional ELT tools, empowering data teams to build and manage their data pipelines with greater flexibility and transparency.
Meltano is an open-source, CLI-first ELT (Extract, Load, Transform) tool designed to provide data engineers with control and visibility over their data pipelines.
Explore all tools that specialize in pipeline orchestration. This domain focus ensures Meltano delivers optimized results for this specific requirement.
Meltano's command-line interface allows for easy scripting and automation of ELT pipelines.
Configuration as code enables version controlling data pipelines using Git.
Meltano's Singer-based connector framework allows easy integration of new data sources and destinations.
Supports custom data transformations using SQL, dbt, or other transformation tools.
Integrates with workflow orchestration tools like Airflow and Dagster.
Ability to mask sensitive data during extraction and load processes.
Install Meltano CLI: `pip install meltano`
Initialize a Meltano project: `meltano init <project_name>`
Add a data extractor: `meltano add extractor <extractor_name>`
Configure the extractor: Edit `meltano.yml` with connection details
Add a data loader: `meltano add loader <loader_name>`
Configure the loader: Edit `meltano.yml` with destination details
Run the ELT pipeline: `meltano run <extractor_name> <loader_name>`
Schedule pipeline runs using a scheduler like Airflow or Dagster
Monitor pipeline execution via logs and alerts
All Set
Ready to go
Verified feedback from other users.
"Meltano receives positive feedback for its flexibility, control, and open-source nature, though some users find the initial setup complex."
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Real-time streaming data pipelines that enhance real-time decision-making and mitigate risks.

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