Migrating from Apache Airflow to Dagster

Guide to migrating from Apache Airflow to Dagster, covering pros, cons, data quality checks, lineage, and learning curve.

By

Jatin Solanki

Updated on

August 3, 2024

So, you're a seasoned data engineer, and you've been using Apache Airflow for quite a while. If you're anything like me, you've had moments of brilliance with it, but also moments of, well, not-so-brilliance. Airflow has its merits, undoubtedly. But, in recent times, I've made the switch to Dagster, and folks, I haven't looked back.

The World of Apache Airflow

Before we get into the Dagster fanfare, let's level-set and give Airflow its due. It's been the go-to for a lot of us for years. Apache Airflow, an open-source platform to programmatically author, schedule, and monitor workflows, has its own set of attractions. The robustness of Airflow is defined by the level of complexity it can handle. It’s wonderful at scheduling complex jobs and has a healthy and growing community (GitHub - apache/airflow).

Airflow's key highlights:

  • Strong Community Support: Airflow has a wide community of users and contributors who continue to improve functions and offer solutions to common issues.
  • Dynamic Pipeline Creation: With Airflow, you can create dynamic pipelines via its Python-oriented nature.
  • Scalable: Airflow has good scalability and can handle a large amount of data volume.

That said, it's not without its disadvantages:

  • High Complexity: Despite its power, Airflow's user interface is complex and can be quite difficult to get used to, particularly for newcomers.
  • Maintenance: Airflow requires regular monitoring and maintenance for effective functioning.
  • Lacks Strong Data Lineage Support: Airflow lacks a concrete data lineage solution, which could be a pain when it comes to tracing data issues.

Enter Dagster

Dagster is a relatively new kid on the block, but it's rapidly becoming a powerful alternative for data orchestration (GitHub - dagster-io/dagster). With a strong emphasis on development, testing, deployment, and monitoring, it's designed for building and managing ETL pipelines, machine learning pipelines, and similar computational workloads.

Here's why I'm championing Dagster:

  • Developer-Focused: Dagster is built with a developer-first mindset. It provides excellent visibility into pipeline execution, configurable execution environments, and local development modes.
  • Data Quality Checks: It includes native support for common patterns in ETL and ML workloads, like automated testing and data quality checks, which can save a lot of development time.
  • Strong Data Lineage Support: Dagster has a solid data lineage system, aiding the tracking of data issues across a pipeline.
  • Flexible Deployment Options: Dagster can run either locally for testing and development or on a server or in a containerized environment for deployment.

Despite these strengths, Dagster isn't without its own set of drawbacks:

  • Less Mature Community: As a newer platform, Dagster's community is smaller and less mature, meaning less support and fewer external resources.
  • New System Learning Curve: Switching to Dagster will involve learning a new system with its own unique principles and architecture.

Switching from Airflow to Dagster

Here's how my journey from Airflow to Dagster went.

The Migration Process

For me, migration started with becoming familiar with the Dagster system. The Dagster documentation provides an in-depth guide to understanding the basic structure, architecture, and working of Dagster.

The real action starts with code migration. Here is a basic Dagster pipeline for your reference

For detailed migration process, I suggest you check out Airflow migration to Dagster.

Deploying Dagster involves running the dagit service and the Dagster daemon process. Check out the Dagster Deployment Overview for comprehensive information.

What I've Gained

Transitioning to Dagster was a calculated risk that paid off. The development and testing process has become streamlined and the data quality checks have drastically reduced the time I spend troubleshooting.

The built-in tools for managing configurations, maintaining data quality, and visualizing data lineage are a godsend. Plus, the local development mode makes it easy to test pipelines before deployment.

What I've Lost

However, there's no denying that Airflow's larger and mature community was a significant advantage. Often, when I stumbled upon an issue in Airflow, someone in the community had already encountered it and provided a solution.

Migrating to a new system is never a cakewalk. Learning the ins and outs of Dagster took time and patience.

Wrapping Up

To sum it up, both Airflow and Dagster have their own sets of strengths and weaknesses. Dagster is a solid choice for those prioritizing development environment, data quality, and visibility into pipeline execution.

If you're considering making the switch, take time to familiarize yourself with Dagster, check out its documentation, and, most importantly, understand its architectural principles.

Remember, every tool has its place, and the choice depends on your unique requirements. Happy data engineering!

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A Data Trust Platform is a unified framework that combines data observability, governance, lineage, and cataloging to ensure financial institutions have accurate, secure, and compliant data. In banking, it enables faster regulatory reporting, safer AI adoption, and new revenue opportunities from data products and APIs.
Why do AI initiatives fail in Latin American banks and fintechs?
Most AI initiatives in LATAM fail due to poor data quality, fragmented architectures, and lack of governance. When AI models are fed stale or incomplete data, predictions become inaccurate and untrustworthy. Establishing a Data Trust Strategy ensures models receive fresh, auditable, and high-quality data, significantly reducing failure rates.
What are the biggest data challenges for financial institutions in LATAM?
Key challenges include: Data silos and fragmentation across legacy and cloud systems. Stale and inconsistent data, leading to poor decision-making. Complex compliance requirements from regulators like CNBV, BCB, and SFC. Security and privacy risks in rapidly digitizing markets. AI adoption bottlenecks due to ungoverned data pipelines.
How can banks and fintechs monetize trusted data?
Once data is governed and AI-ready, institutions can: Reduce OPEX with predictive intelligence. Offer hyper-personalized products like ESG loans or SME financing. Launch data-as-a-product (DaaP) initiatives with anonymized, compliant data. Build API-driven ecosystems with partners and B2B customers.
What is data dictionary example?
A data dictionary is a centralized repository that provides detailed information about the data within an organization. It defines each data element—such as tables, columns, fields, metrics, and relationships—along with its meaning, format, source, and usage rules. Think of it as the “glossary” of your data landscape. By documenting metadata in a structured way, a data dictionary helps ensure consistency, reduces misinterpretation, and improves collaboration between business and technical teams. For example, when multiple teams use the term “customer ID”, the dictionary clarifies exactly how it is defined, where it is stored, and how it should be used. Modern platforms like Decube extend the concept of a data dictionary by connecting it directly with lineage, quality checks, and governance—so it’s not just documentation, but an active part of ensuring data trust across the enterprise.
What is an MCP Server?
An MCP Server stands for Model Context Protocol Server—a lightweight service that securely exposes tools, data, or functionality to AI systems (MCP clients) via a standardized protocol. It enables LLMs and agents to access external resources (like files, tools, or APIs) without custom integration for each one. Think of it as the “USB-C port for AI integrations.”
How does MCP architecture work?
The MCP architecture operates under a client-server model: MCP Host: The AI application (e.g., Claude Desktop or VS Code). MCP Client: Connects the host to the MCP Server. MCP Server: Exposes context or tools (e.g., file browsing, database access). These components communicate over JSON‑RPC (via stdio or HTTP), facilitating discovery, execution, and contextual handoffs.
Why does the MCP Server matter in AI workflows?
MCP simplifies access to data and tools, enabling modular, interoperable, and scalable AI systems. It eliminates repetitive, brittle integrations and accelerates tool interoperability.
How is MCP different from Retrieval-Augmented Generation (RAG)?
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What is a data dictionary?
A data dictionary is a centralized repository that provides detailed information about the data within an organization. It defines each data element—such as tables, columns, fields, metrics, and relationships—along with its meaning, format, source, and usage rules. Think of it as the “glossary” of your data landscape. By documenting metadata in a structured way, a data dictionary helps ensure consistency, reduces misinterpretation, and improves collaboration between business and technical teams. For example, when multiple teams use the term “customer ID”, the dictionary clarifies exactly how it is defined, where it is stored, and how it should be used. Modern platforms like Decube extend the concept of a data dictionary by connecting it directly with lineage, quality checks, and governance—so it’s not just documentation, but an active part of ensuring data trust across the enterprise.
What is the purpose of a data dictionary?
The primary purpose of a data dictionary is to help data teams understand and use data assets effectively. It provides a centralized repository of information about the data, including its meaning, origins, usage, and format, which helps in planning, controlling, and evaluating the collection, storage, and use of data.
What are some best practices for data dictionary management?
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How does a business glossary differ from a data dictionary?
A business glossary covers business terminology and concepts for an entire organization, ensuring consistency in business terms and definitions. It is a prerequisite for data governance and should be established before building a data dictionary. While a data dictionary focuses on technical metadata and data objects, a business glossary provides a common vocabulary for discussing data.
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What are the key pillars of Data Observability?
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What is Data Observability and why is it important?
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