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Extract
Fetch data from anywhere: database, API, files, and more, all in the Singer format.
Open source data integration has never been easier or faster. With Meltano, you can extract data from GitHub and load it into PostgreSQL (or Snowflake, BigQuery, Redshift, etc.) in just 90 seconds from initializing a new Meltano project to viewing the loaded data in the resulting Meltano project repository.
Self Hosted
Open Source
CI/CD
Extract and Load
Isolated Dev Environments
Inline Hashing for PII
Pipeline Testing
Pipelines as Code
Connectors
300+
160+
170+
ELT means Extract, Load, Transform. It’s a method of data replication and transformation used to perform data integration at any scale. The purpose of ELT is to extract specific data—such as customer information or billing records—from its source and deliver it to its end point in the fastest, most reliable way possible.
ELT is a three-step process:
- Extract the raw, unorganized data from the source location
- Load the unprocessed data into its target destination, such as a data warehouse
- Transform the data into an accessible, comprehensible format at its target destination, such as an analytics report
ELT does a few things very well. Ingestion speeds are high since remote processing isn’t a prerequisite to loading. In addition, raw data can deliver more insights due to its historical nature, since timelines and metrics are readily available for mining.
ELT is also highly scalable since it can tap into cloud resources and native processing power, and adding more data sources isn’t a problem.
Benefits from running your ELT pipeline with Meltano:
- Data source identification
- Automated extraction and loading
- Determine analytical and project goals
- Create data models via transformations
- Extract insights via analysis
