May 17, 2024 • 4 minute read •

What is Dagster: A Guide to the Data Orchestrator

Name: Pete Hunt
Handle: @floydophone

This guide provides a high-level overview of Dagster, the solution for defining, maintaining, and observing the key data assets your organization depends on. We'll explain our philosophy, core components, the benefits Dagster offers, and why it stands out as the ideal tool for modern data orchestration.

Whether you're a seasoned data engineer or new to the field, this guide will help you gain an understanding of Dagster and its potential to transform your data processes.

Addressing Critical Data Challenges

Data engineering teams are facing challenges on several fronts:

Executives, excited by the promise of what data can do in their organizations, are setting higher expectations and shorter timelines
Data volumes and sources are exploding, resulting in an increasingly complex and heterogeneous data environment, with teams needing to support the many technologies and stakeholders involved in implementing data science, ML and AI.
Data security and governance remain paramount.

Dagster has been designed to help data engineering teams tackle these challenges with confidence. With Dagster, you adopt both a framework and a system for building a data platform where teams can collaborate to:

Define which data assets are required, and how they are created.
Observe, test, and iterate on the production of these assets.
Enforce data quality rules.
Establish reliable, optimized scheduling for when these assets get updated.
Report on the state of all assets managed by Dagster.

In particular, 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.

By choosing Dagster, you’re investing in a tool that both meets your current data orchestration needs and adapts to future challenges. Its thoughtful design, combined with a commitment to developer experience and community support, makes Dagster a standout choice for organizations looking to build sustainable and scalable data platforms.

What is Dagster?

At its core, Dagster is a framework and a system designed to bring structure and efficiency to data platforms. It allows data engineering teams to define, schedule, and monitor data pipelines in a highly observable, scalable, and sustainable way. It also facilitates collaboration across data teams.

Dagster's design allows it to model and manage the flow of data and the execution of compute tasks across various systems, which can include tasks such as data ingestion, transformation, and analysis. This can apply to things like:

Managing customer data for targeted communications and customer experiences
Data science model training
Business intelligence
Data governance
Data quality and testing
Data process optimization (i.e. reducing costs)

Dagster ensures the availability, usability, and security of data -- crucial for maintaining standards, managing visibility over data sets, and facilitating compliance with data privacy laws.

The Philosophy Behind Dagster

For far too long, data orchestration best practices have been adopted late in the development lifecycle. To many, it primarily serves as a mechanism to deploy your data pipelines to production, often reduced to simply coordinating and automating the flow of data from one point to another.

This mindset undervalues data orchestration’s key role at every stage of the software development lifecycle, which can accelerate teams of all shapes and sizes at every step of the way.

Said another way: teams can unlock massive gains in developer productivity and happiness by adopting a data orchestrator like Dagster on day one.

In the design of Dagster, we’ve put a strong emphasis on the development experience. This means providing a tool that supports engineers throughout the entire development lifecycle—from local development to production monitoring. Dagster is built to be developer-centric, with a focus on testing, maintainability, and a rich development environment.

Tip: For a more in-depth look at what Dagster believes about data platforms and orchestration, check out this piece by our Lead Engineer, Sandy Ryza.

Core Components of Dagster

Dagster introduces several key abstractions that form the building blocks of any data workflow:

Assets: Data assets are a fundamental concept in Dagster. They represent the tangible outputs of your data pipelines, and are ultimately the end product your stakeholders care about. By focusing on data assets as your primary concern, you can track the lineage and dependencies of your data, and observe the process of building those assets over time.
Pipelines: In Dagster pipelines are represented by a high-level direct acyclic graph (or DAG) of data assets and computation that defines how data flows through various processing steps and how assets are materialized (i.e. computed).
Resources: Resources in Dagster are any external system that the pipeline depends on to execute such as database connections, APIs, or compute resources. They can be configured per pipeline run, allowing for flexible execution environments. Dagster can connect to various data storage systems including data lakes and relational databases.
Schedules and Sensors: Dagster allows you to define schedules to run your data pipelines at a specific frequency, and sensors to trigger pipeline runs based on external events. The scheduling system in Dagster is rapidly evolving to be more context-aware, allowing you to optimize the execution of your pipelines.
Partitions and Partition Sets: For batch computations that need to be run over a dataset sliced by time or another dimension, Dagster provides partitions and partition sets to organize and execute these computations. These in turn allow you to eliminate redundant compute, and speed up your prototyping and testing.

Such components facilitate the construction of scalable and easily modifiable pipelines. Dagster’s explicit declaration of dependencies and data types aids in early error detection, enhancing pipeline reliability and minimizing debugging time.

Dagster+

Building upon the foundation of Dagster, Dagster+ is an enhanced offering that brings advanced data asset management, operational workflows, and enhanced security and compliance features to enterprise-scale data operations. With Dagster+, teams gain access to premium tools that streamline the entire lifecycle of data orchestration, from development to production, while ensuring that data governance and security standards are met. Subscribers also benefit from priority support, ensuring quick resolution of issues and smooth operation of data pipelines. This enables Dagster to empower data teams and drive strategic business outcomes with confidence.

To learn more about the advantages of Dagster+ and how it can elevate your data orchestration, visit our page on Dagster+ or learn about its launch here.

Case Studies and User Testimonials

Don't just take our word for it, hear from other users about their experiences. These case studies from various organizations provide insights into how Dagster has made a significant impact on their data operations.

Case Study 1: BenchSci

BenchSci, a bioinformatics company, significantly improved its data workflows by adopting Dagster. They faced challenges with their previous system, including a lack of visibility and difficulty debugging. After moving to Dagster, they gained a clear view of their data dependencies and the ability to test locally, resulting in faster debugging and overall development time. You can read more about their journey with Dagster in this blog post.

Case Study 2: SimpliSafe

SimpliSafe, a home security company, enhanced its data platform by leveraging Dagster. The transformation enabled them to streamline their data processes, improve visibility, and create more reliable data pipelines. You can read more about their experience with Dagster in this blog post.

Case Study 3: Catalyst Cooperative

Catalyst Cooperative, an energy data consultancy, used Dagster to manage its complex data pipelines. With Dagster, they were able to increase their efficiency and create more reliable workflows. You can read more about their experience with Dagster in this blog post.

Conclusion

Congratulations, you've officially met Dagster.

Dagster's design, emphasis on developer experience, and community support make it a standout choice for organizations looking to build sustainable and scalable data infrastructures. Its flexibility, scalability, and focus on observability can transform your data pipeline development and enable you to make faster, data-driven decisions.

Check out our product page to explore Dagster further and see how it can revolutionize your data operations.

We're always happy to hear your feedback, so please reach out to us! If you have any questions, ask them in the Dagster community Slack (join here!) or start a Github discussion. If you run into any bugs, let us know with a Github issue. And if you're interested in working with us, check out our open roles!

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