Dagster is one of many Data Orchestrators. It operates in a declarative and data-aware manner, offering unique capabilities in data orchestration.

Dagster is an orchestrator that’s designed for developing and maintaining data assets, such as tables, data sets, machine learning models, and reports.

You declare functions that you want to run and the data assets that those functions produce or update. Dagster then helps you run your functions at the right time and keep your assets up-to-date.

Dagster is built to be used at every stage of the Data Engineering Lifecycle - local development, unit tests, integration tests, staging environments, all the way up to production.

More on Dagster Docs.

Discover the ease of migration from Apache Airflow to Dagster with this insightful YouTube video.

Dagster was built to deliver the following:

Improved development velocity with a clear framework for building, testing, and deploying assets.Enhanced Observability and Monitoring: Dagster offers detailed insights into pipeline runs. This includes logs, execution timing, and the capability to trace the lineage of data assets.Alignment with Best Practices: Dagster is designed to foster the adoption of best practices in software and data engineering, including testability, modularity, code reusability and version control.Rapid Debugging: Dagster employs a structured approach to error handling, enabling engineers to swiftly pinpoint and rectify issues.Greater Reliability and Error Handling: Dagster pipelines consistently run as expected and maintain data quality by design.Flexible Integration with Other Tools and Systems: As data platforms have become more heterogeneous, Dagster provides options for orchestration across technologies and compute environments.Scalability and Performance: Dagster can seamlessly scale from a single developer’s laptop to a full-fledged production-grade cluster, thanks to its execution model that supports both parallel and distributed computing.Community and Support: Dagster is an actively developed platform with robust documentation and training resources, and a growing, vibrant community.

More on What is Dagster: A Guide to the Data Orchestrator | Dagster Blog.

# Escapinghttps://www.ssp.sh/brain/Pasted%20image%2020231009082638.png/Pasted%20image%2020231009082638.png"> Key insights from the launch week 2023-10-09 are available

In my workflow, Dagster is integral for all Python-related tasks. Its framework encourages functional programming practices, helping write code that is declarative, abstracted, idempotent, and type-checked. This approach aids in early error detection. Dagster’s features include simplified unit testing and tools for creating robust, testable, and maintainable pipelines. For more insights, see my article “The Shift From a Data Pipeline to a Data Product” in Data Orchestration Trends- The Shift From Data Pipelines to Data Products.

Learning functional programming languages has reshaped my thinking process. For those interested in integrating functional programming within Python, explore Python and Functional Programming. Origin: Simon Späti on LinkedIn: #dataengineering #idempotent #declarative

# From Imperative to Declarative

We transition from imperative to declarative programming (refer to Declarative vs Imperative). This shift is akin to the movement towards declarative entities in Frontend and DevOps. In data, the declarative entity is the Data Asset (e.g., dashboard, table , report, ML model).

Before implemhttps://www.ssp.shhttps://www.ssp.sh/brain/Pasted%20image%2020220428094047.pnglustrated:

Issues like duplicated data and inconsistent intervals:

After adopting Dagster and Data Assets:

Asset view transformation:
Each box represents a physical asset, not merely a task or operation, differentiating it from Apache Airflow.This lehttps://www.ssp.shhttps://www.ssp.sh/brain/Pasted%20image%2020220428100421.pngng in a more scalable graph.
Integration of SQL upstream logic with actual data assets:

This approach elevates the Modern Data Stack to a new level. For a comprehensive understanding, see Modern Data Stack.

# Conclusion

Dagster’s new layout with clear layers of abstractions, integrating stateful assets while running on any compute (processing layer) looks really cool. https://www.ssp.shhttps://www.ssp.sh/brain/Pasted%20image%2020241121143000.webpedglm4muiyzty2snc55ysuqx/post/3lbhli67oek2u" rel="noopener">Bsky

Dagster Components is a new low-code approach to building with Dagster. The promise: Modern data platform teams face a constant tension: Build platforms that are bulletproof, standardized, and customizable—while somehow still remaining accessible to a wide range of stakeholders.

Components provide a low-code YAML interface for your users, backed by tools that support software engineering best practices and give data platform teams complete control. Giving you:

• Build maintainable, low-code data platforms
• Empower self-serve data workflows without sacrificing standards
• Customize and create new components to fit your stack

E.g. Claude Code with MCP implementation with Dagster, see check Demo. ^ceb890

Explore external schedule management with Process Manager for Dagster.

The event on

Pedram’s demonstration using Steampipe: Find more details in the GitHub repository: dagster/README.md at master · dagster-io/dagster · GitHub

Like Modal, Hex, etc. are managed within one repo:
GitHub - dagster-io/community-integrations: Community supported integrations for the Dagster platform.

Learn more about Dagster Cloud.

The project was started in 2018 by Nick Shrock and was conceived as a result of a need identified by him while working at Facebook. One of the goals of Dagster has been to provide a tool that removes the barrier between pipeline development and pipeline operation, but during this journey, he came to link the world of data processing with business processes.

See more on Bash-Script vs. Stored Procedure vs. Traditional ETL Tools vs. Python-Script - 📖 Data Engineering Design Patterns (DEDP).

Check out more on awesome-dagster, dagster-open-platform and devrel-project-demos.

These integrations will automatically trigger dashboards to be updated when the upstream task or asset is updated:

Dagster migration to newer versions

Origin:
References: Dagster Wiki
Created: