Dagster vs Orchest
Detailed comparison between the open source software named 'Dagster' and 'Orchest'. Dagster and Orchest are both open source platforms for building data pipelines, but they differ in their approach to pipeline orchestration and management. Dagster is a Python-based data orchestration platform that provides a unified view of data pipelines across different systems. It allows users to define, test, and execute data pipelines in a reproducible and scalable way. Dagster emphasizes data lineage and provenance, making it easy to understand the data flow through a pipeline and troubleshoot issues. It also provides built-in support for managing dependencies and versioning artifacts, making it easy to deploy pipelines to different environments. Orchest, on the other hand, is a workflow management platform that provides a visual interface for designing and managing data pipelines. It is built on top of Kubernetes and provides a scalable and fault-tolerant platform for running workflows. Orchest supports a wide variety of data sources and provides built-in support for common data operations like filtering, transforming, and aggregating data. It also allows users to define custom operators using Python or any other language that can be containerized. Here are some key differences between Dagster and Orchest: Approach to pipeline orchestration: Dagster provides a unified view of data pipelines across different systems, whereas Orchest focuses on running workflows on Kubernetes. Data lineage and provenance: Dagster emphasizes data lineage and provenance, making it easy to understand the data flow through a pipeline and troubleshoot issues. Orchest provides a visual interface for managing workflows but does not have the same level of data lineage and provenance tracking. Programming language: Dagster is built in Python and provides a Python-based API for defining pipelines. Orchest supports a variety of languages and provides an API for defining custom operators. Scalability: Orchest is built on top of Kubernetes and provides a scalable and fault-tolerant platform for running workflows. Dagster can also be run on Kubernetes, but it does not have the same level of built-in scalability features. In summary, Dagster and Orchest are both powerful platforms for building data pipelines, but they differ in their approach to pipeline orchestration, data lineage, and scalability. Dagster is a more Python-focused platform that provides a unified view of pipelines, while Orchest is a Kubernetes-based workflow management platform that provides a visual interface for designing and managing workflows.