Apache Airflow vs Dagster: Side by Side Comparison

Compare Apache Airflow and Dagster for data workflows. Airflow excels in task generation; Dagster offers strong data validation and ML integration.

By

Jatin Solanki

Updated on

October 2, 2024

Apache Airflow and Dagster are open-source platforms used for managing and scheduling data workflows. While they have similar goals, they differ in their approach and features. Apache Airflow is task-based, with dynamic task generation and a web-based user interface, while Dagster is pipeline-based, with strong data validation and error handling and integration with ML frameworks. When choosing between the two platforms, consider your specific needs and use case. Apache Airflow is best for dynamic task generation and integration with tools like Spark and Hadoop, while Dagster is best for strong data validation and error handling or integration with ML frameworks like TensorFlow or PyTorch.

Part 1: Introduction and Overview

  • Introduce Apache Airflow and Dagster, their features, and their intended use cases.
  • Explain the importance of comparing the performance of these two platforms.
  • Provide an overview of what the rest of the article will cover.

Part 2: Comparing Apache Airflow and Dagster

  • Compare the features and performance of Apache Airflow and Dagster.
  • Discuss the strengths and weaknesses of each platform.
  • Provide sample codes for both platforms.

Part 3: Conclusion and Recommendations

  • Summarize the key points of the article.
  • Provide recommendations for which platform to choose in different situations.
  • Conclude in a diplomatic fashion.

Let's get started!

Part 1: Introduction and Overview

Apache Airflow and Dagster are both open-source platforms designed to manage and schedule data workflows. They allow data engineers to define complex pipelines, track the progress of those pipelines, and manage dependencies between tasks.

Comparing the performance of these two platforms is important because data engineers need to choose the best tool for their specific use case. Understanding the strengths and weaknesses of each platform can help data engineers make informed decisions about which platform to use.

In this article, we will compare the features and performance of Apache Airflow and Dagster. We'll look at sample codes for both platforms and provide recommendations for which platform to choose in different situations.

Part 2: Comparing Apache Airflow and Dagster

Apache Airflow and Dagster have similar goals and features, but they approach those goals in slightly different ways. Here's a breakdown of some of the key features of each platform:

Apache Airflow:

  • Task-based workflow definition
  • Dynamic task generation
  • Built-in operators for common tasks (e.g., PythonOperator, BashOperator, etc.)
  • Web-based user interface for monitoring and managing workflows
  • Large community and ecosystem of plugins and integrations

Dagster:

  • Type-checked, composable pipeline definitions
  • Automatic tracking of dependencies between tasks
  • Built-in data validation and error handling
  • Integration with ML frameworks like TensorFlow and PyTorch
  • Strong emphasis on testing and reproducibility

Let's take a closer look at some sample code for each platform.

Sample code - Airflow


from airflow import DAG
from airflow.operators.bash_operator import BashOperator
from datetime import datetime

default_args = {
    'owner': 'airflow',
    'depends_on_past': False,
    'start_date': datetime(2023, 3, 27),
    'email_on_failure': False,
    'email_on_retry': False,
    'retries': 1,
    'retry_delay': timedelta(minutes=5),
}

dag = DAG(
    'my_dag',
    default_args=default_args,
    description='A simple tutorial DAG',
    schedule_interval=timedelta(days=1),
)

t1 = BashOperator(
    task_id='print_date',
    bash_command='date',
    dag=dag,
)

t2 = BashOperator(
    task_id='sleep',
    bash_command='sleep 5',
    retries=3,
    dag=dag,
)

t2.set_upstream(t1)

This code defines a simple DAG with two tasks: one that prints the current date and time, and another that sleeps for five seconds. The BashOperator is used to run Bash commands, but Airflow has many other built-in operators for different types of tasks.

Sample code for Dagster:


from dagster import pipeline, solid

@solid
def load_data(context):
    return {'data': ...} #
@solid
def preprocess_data(context, data):
    return preprocess(data)

@solid
def train_model(context, preprocessed_data):
    return train(preprocessed_data)

@solid
def evaluate_model(context, trained_model):
    return evaluate(trained_model)

@pipeline
def my_pipeline():
    evaluate_model(train_model(preprocess_data(load_data())))

This code defines a pipeline with four tasks: load_data, preprocess_data, train_model, and evaluate_model. Each task is defined as a solid function, and the pipeline is defined using a @pipeline decorator. Note that the train_model task takes the output of the preprocess_data task as input, and the evaluate_model task takes the output of the train_model task as input. Dagster automatically tracks these dependencies and ensures that tasks are run in the correct order.

So how do these two platforms compare in terms of performance and features? Here are some things to consider:

  • Task-based vs. pipeline-based: Apache Airflow is task-based, which means you define each individual task and its dependencies separately. Dagster is pipeline-based, which means you define the entire pipeline as a single unit, with tasks nested inside it. This can make it easier to manage dependencies in complex pipelines.
  • Dynamic task generation: Apache Airflow allows you to generate tasks dynamically based on data or other factors. Dagster does not have this feature, which can be a limitation in some use cases.
  • Error handling and validation: Dagster has built-in support for data validation and error handling, which can be very useful in data-intensive workflows. Apache Airflow does not have this feature, although it does have error handling mechanisms for individual tasks.
  • ML framework integration: Dagster has built-in integration with ML frameworks like TensorFlow and PyTorch. Apache Airflow does not have this feature, although it does have integrations with other tools like Spark and Hadoop.

Overall, both platforms have their strengths and weaknesses. Apache Airflow is a more mature platform with a larger community and ecosystem, while Dagster has some innovative features that make it a good choice for data-intensive workflows.

Part 3: Conclusion and Recommendations

In conclusion, choosing between Apache Airflow and Dagster depends on your specific use case and needs. If you need a more mature platform with a larger community and ecosystem, Apache Airflow may be the best choice. If you need strong data validation and error handling, or integration with ML frameworks, Dagster may be a better choice.

Here are some specific recommendations:

  • Choose Apache Airflow if you need to generate tasks dynamically or if you need to integrate with tools like Spark or Hadoop.
  • Choose Dagster if you need strong data validation and error handling, or if you need integration with ML frameworks like TensorFlow or PyTorch.
  • Consider both platforms if you need to manage complex workflows with many dependencies and moving parts.

Ultimately, both Apache Airflow and Dagster are powerful tools for managing data workflows, and choosing between them comes down to your specific needs and use case. We hope this article has provided a useful comparison of the features and performance of these two platforms.

Data observability is crucial for maintaining the reliability and accuracy of data workflows, and solutions such as decube can help provide necessary visibility and insights into pipeline performance. Data engineers should explore available solutions like decube to ensure observability in their pipelines. You can signup for decube for free 30 days trial

What is a Data Trust Platform in financial services?
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)?
Unlike RAG—which retrieves documents for LLM consumption—MCP enables live, interactive tool execution and context exchange between agents and external systems. It’s more dynamic, bidirectional, and context-aware.
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?
Best practices for data dictionary management include assigning ownership of the document, involving key stakeholders in defining and documenting terms and definitions, encouraging collaboration and communication among team members, and regularly reviewing and updating the data dictionary to reflect any changes in data elements or relationships.
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.
What is the difference between a data catalog and a data dictionary?
While a data catalog focuses on indexing, inventorying, and classifying data assets across multiple sources, a data dictionary provides specific details about data elements within those assets. Data catalogs often integrate data dictionaries to provide rich context and offer features like data lineage, data observability, and collaboration.
What challenges do organizations face in implementing data governance?
Common challenges include resistance from business teams, lack of clear ownership, siloed systems, and tool fragmentation. Many organizations also struggle to balance strict governance with data democratization. The right approach involves embedding governance into workflows and using platforms that unify governance, observability, and catalog capabilities.
How does data governance impact AI and machine learning projects?
AI and ML rely on high-quality, unbiased, and compliant data. Poorly governed data leads to unreliable predictions and regulatory risks. A governance framework ensures that data feeding AI models is trustworthy, well-documented, and traceable. This increases confidence in AI outputs and makes enterprises audit-ready when regulations apply.
What is data governance and why is it important?
Data governance is the framework of policies, ownership, and controls that ensure data is accurate, secure, and compliant. It assigns accountability to data owners, enforces standards, and ensures consistency across the organization. Strong governance not only reduces compliance risks but also builds trust in data for AI and analytics initiatives.
What is the difference between a data catalog and metadata management?
A data catalog is a user-facing tool that provides a searchable inventory of data assets, enriched with business context such as ownership, lineage, and quality. It’s designed to help users easily discover, understand, and trust data across the organization. Metadata management, on the other hand, is the broader discipline of collecting, storing, and maintaining metadata (technical, business, and operational). It involves defining standards, policies, and processes for metadata to ensure consistency and governance. In short, metadata management is the foundation—it structures and governs metadata—while a data catalog is the application layer that makes this metadata accessible and actionable for business and technical users.
What features should you look for in a modern data catalog?
A strong catalog includes metadata harvesting, search and discovery, lineage visualization, business glossary integration, access controls, and collaboration features like data ratings or comments. More advanced catalogs integrate with observability platforms, enabling teams to not only find data but also understand its quality and reliability.
Why do businesses need a data catalog?
Without a catalog, employees often struggle to find the right datasets or waste time duplicating efforts. A data catalog solves this by centralizing metadata, providing business context, and improving collaboration. It enhances productivity, accelerates analytics projects, reduces compliance risks, and enables data democratization across teams.
What is a data catalog and how does it work?
A data catalog is a centralized inventory that organizes metadata about data assets, making them searchable and easy to understand. It typically extracts metadata automatically from various sources like databases, warehouses, and BI tools. Users can then discover datasets, understand their lineage, and see how they’re used across the organization.
What are the key features of a data observability platform?
Modern platforms include anomaly detection, schema and freshness monitoring, end-to-end lineage visualization, and alerting systems. Some also integrate with business glossaries, support SLA monitoring, and automate root cause analysis. Together, these features provide a holistic view of both technical data pipelines and business data quality.
How is data observability different from data monitoring?
Monitoring typically tracks system metrics (like CPU usage or uptime), whereas observability provides deep visibility into how data behaves across systems. Observability answers not only “is something wrong?” but also “why did it go wrong?” and “how does it impact downstream consumers?” This makes it a foundational practice for building AI-ready, trustworthy data systems.
What are the key pillars of Data Observability?
The five common pillars include: Freshness, Volume, Schema, Lineage, and Quality. Together, they provide a 360° view of how data flows and where issues might occur.
What is Data Observability and why is it important?
Data observability is the practice of continuously monitoring, tracking, and understanding the health of your data systems. It goes beyond simple monitoring by giving visibility into data freshness, schema changes, anomalies, and lineage. This helps organizations quickly detect and resolve issues before they impact analytics or AI models. For enterprises, data observability builds trust in data pipelines, ensuring decisions are made with reliable and accurate information.

Table of Contents

Read other blog articles

Grow with our latest insights

Sneak peek from the data world.

Thank you! Your submission has been received!
Talk to a designer

All in one place

Comprehensive and centralized solution for data governance, and observability.

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
decube all in one image