Dagster

Define assets representing data extracts from SaaS APIs.

Define assets representing data transformations using dbt.

Define an asset representing the final data warehouse table.

Orchestrate the pipeline to automatically extract, transform, and load data.

Monitor pipeline health and data quality using Dagster's observability tools.

Define assets representing data preprocessing steps.

Define an asset representing model training using a framework like TensorFlow or PyTorch.

Define an asset representing model evaluation and validation.

Define an asset representing model deployment to a serving platform.

Orchestrate the pipeline to automatically train, evaluate, and deploy models.

Define assets representing data quality checks using tools like Great Expectations.

Define assets representing data validation rules.

Orchestrate the pipeline to automatically run data quality checks.

Alert stakeholders when data quality issues are detected.

Track data quality metrics over time using Dagster's observability tools.

Define assets representing data ingestion from streaming sources.

Define assets representing data transformations and aggregations.

Define assets representing data storage in real-time databases.

Orchestrate the pipeline to automatically process streaming data in real-time.

Monitor pipeline performance and data latency using Dagster's observability tools.

Use Dagster's data catalog features to track data assets and metadata.

Automatically generate documentation for data assets using Dagster's lineage tools.

Enable users to discover and understand data assets through Dagster's UI.

Integrate Dagster with external data catalogs for enhanced data governance.

Manage data access and permissions using Dagster's role-based access control features.