You would use Dagster when you need to build and manage more complex data workflows that require detailed orchestration, scheduling, and monitoring of various data processing tasks. For instance, if you're developing a pipeline that transforms raw data from multiple sources, applies machine learning models, and loads it into a data warehouse with strict scheduling requirements, Dagster provides a more robust framework to handle this complexity compared to Meltano.
Meltano is ideal for teams looking for a straightforward solution to orchestrate ELT workflows with built-in integrations and a focus on data extraction and loading. It's particularly useful for smaller teams or projects where quick deployment and ease of use are priorities. For example, if you're a small startup needing to quickly set up a data pipeline from a SaaS application to a data warehouse without extensive engineering resources, Meltano simplifies the process with its pre-built components.