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See why hundreds of industry leaders trust Secoda to unlock their data's full potential.
See why hundreds of industry leaders trust Secoda to unlock their data's full potential.
Dagster is a cloud-based data pipeline orchestrator that helps users develop and maintain data assets like tables, data sets, machine learning models, and reports. It provides a single pane of glass for data platforms, allowing users to monitor jobs, debug runs, inspect assets, and launch backfills.
Dagster helps developers build scalable and maintainable workflows by focusing on reliability, testing, and metadata management. Users declare functions that they want to run and the data assets that those functions produce or update. Dagster then helps users run their functions at the right time and keep their assets up-to-date.
Dagster is used by data teams from startups to Fortune 500 companies. It's available on PyPI and officially supports Python 3.8, Python 3.9, Python 3.10, and Python 3.11.
Dagster offers several features including Integrated lineage and observability, A declarative programming model, Testability, Fully serverless or hybrid deployments, and Native branching.
Secoda is also an integration platform that allows users to monitor data resource usage levels from Dagster, a cloud-native data orchestrator. It also offers integration with Dagster, which allows users to automate workflows using actions and triggers. Triggers activate workflows based on specific schedules, such as hourly, daily, or custom intervals. Actions include various operations, such as filtering and updating metadata.
Integrating Dagster with Secoda allows users to easily search, index, and discover data, automate data preparation and governance, analyze data with Secoda, simplify data access, unlock insights and value within data, add further context to Dagster Assets (Name, Description, Type) and Asset groups (Name), and use Secoda and Dagster together for data migration.
Secoda acts as a centralized platform for managing a company's data knowledge, bringing together data catalog, lineage, documentation, and monitoring.