Introduction
Dagster is a modern data orchestration framework designed to build robust, testable, and observable pipelines using a Python-first, declarative programming model. Dagster offers more than traditional task schedulers: it brings software engineering best practices—such as type safety, modular design, asset tracking, and test-driven development—into the world of data workflows. At Cake, Dagster is used to coordinate data engineering, ML ops, and analytics workloads across services, empowering teams to reason about and operate pipelines with confidence.
Key benefits of using Dagster within the Cake platform include:
Software-Defined Assets (SDAs): Treats datasets, models, and metrics as first-class assets with lineage, versioning, and dependency tracking—making pipelines composable and observable.
Type-Safe, Pythonic Definitions: Pipelines are defined as modular Python functions (ops) and graphs, with built-in type validation and config schemas.
Integrated Observability and Logging: Provides rich runtime visibility via the Dagster UI, including execution plans, logs, metadata, and error surfaces for every step.
Dynamic Scheduling and Re-execution: Supports granular retries, conditional logic, partitioning, and sensor-based triggering for real-time and batch workflows.
Environment Agnostic and Scalable: Deploys on local dev, Kubernetes, or cloud-native environments, and integrates easily with tools like dbt, Airflow, Spark, and MLflow.
Dagster orchestrates workflows such as daily ingestion pipelines, DBT transformations, model retraining schedules, RAG index builds, and evaluation jobs. It replaces brittle cron-based DAGs with fully observable and testable workflows, while providing fine-grained control over task execution and data lineage. By adopting Dagster, you can ensure its data and ML workflows are modular, reliable, and production-grade—empowering teams to operate with transparency, scale confidently, and iterate faster.