AI - Dagster
Build and execute complex data pipelines using Python in an open-source platform. Features orchestration, checkpoints, recovery, dynamic scheduling, ML integration, and cloud partnerships.
- Name
- Dagster -https://github.com/dagster-io/dagster
- Last Audited At
About Dagster
Dagster is an open-source platform developed for building and running data pipelines using Python. They enable data teams to develop, execute, and troubleshoot complex data processing workflows with ease. Dagster's flexible design allows users to define their data processing logic as code, which can be reused across various projects and environments.
Their platform offers key features such as:
- Data pipeline orchestration: Enables users to manage the execution of tasks in a complex workflow, ensuring that dependencies are properly handled.
- Checkpoints and recoverability: Provides mechanisms for storing intermediate results, allowing pipelines to resume from where they left off after failures.
- Dynamic pipeline scheduling: Allows teams to prioritize pipeline runs based on business needs or availability of resources.
Dagster's AI capabilities come into play when using their machine learning (ML) integration. This integration enables users to easily add ML model training and prediction tasks to their pipelines. It also provides features for handling ML experimentation, such as hyperparameter tuning and parallel experiment execution.
Notable partnerships for Dagster include collaborations with Amazon Web Services (AWS), Google Cloud Platform, and Microsoft Azure, which allow their users to seamlessly integrate these cloud providers into their pipelines. Dagster is used by various organizations in industries such as healthcare, finance, and technology to manage and process large amounts of data for analytics, reporting, and machine learning applications.