6 Best Dagster Alternatives
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6 Best Dagster Alternatives

Steven Johnson
Steven Johnson

In the ever-evolving world of data engineering, the search for efficient and reliable tools to manage complex data workflows is ongoing. Dagster, a popular data orchestrator, has become a favorite among data professionals due to its robust and user-friendly features. However, it's essential to explore and consider other options that might be a better fit for your unique requirements. In this blog post, we will dive into some of the top alternatives to Dagster, discussing their strengths and weaknesses to help you make an informed decision when selecting the right data orchestration platform for your needs. So, join us as we embark on a journey to uncover some of the best Dagster alternatives available in the market today.

Apache Airflow

Apache Airflow is a widely-adopted open-source platform, favored by data engineers and teams for its workflow management and scheduling capabilities. Users can author, schedule, and monitor data pipelines programmatically using Directed Acyclic Graphs (DAGs), ensuring efficient management of intricate data processing workflows. Airflow boasts numerous built-in integrations with well-known data processing tools and platforms like Apache Spark, Hadoop, and various cloud services, making it a potent and versatile solution for handling large-scale data workflows.

Utilizing Airflow's programmable DAGs, users can define and visualize elaborate data workflows, allowing them to pinpoint and address workflow bottlenecks and inefficiencies. The platform's impressive scheduling features further augment its value, empowering data engineering teams to automate and orchestrate data processing tasks effortlessly. Despite its widespread use, Airflow can be difficult for some users to deploy and configure, presenting a steep learning curve for novices. However, the platform's comprehensive community support and rich feature set establish it as a dependable and robust option for managing intricate data workflows, serving as a viable Dagster alternative.

Check out Airflow:

Website | Documentation

Source: https://airflow.apache.org/docs/apache-airflow/stable/ui.html

Azure Data Factory

Azure Data Factory is a serverless data integration service that provides a dependable and cost-efficient solution for data teams seeking compatibility with Microsoft-specific technologies. As a pay-as-you-go cloud service, it offers on-demand scalability, ensuring both flexibility and cost-effectiveness. The platform focuses on no-code pipeline components, allowing users to construct ETL/ELT pipelines with integrated Git and CI/CD without any coding. With over 90 built-in connectors, Azure Data Factory supports the ingestion of on-premises and SaaS data, simplifying orchestration and monitoring on a large scale.

Azure Data Factory also boasts solid integrations with the broader Microsoft Azure platform, making it an optimal choice for organizations looking for compatibility with Microsoft solutions or those already utilizing Azure services. However, the platform's no-code methodology might not be ideal for data engineers who desire greater control over data processing workflows. Regardless, Azure Data Factory remains a versatile and trustworthy serverless data integration service that offers an accessible approach to ETL/ELT pipeline

Check out Azure Data Factory:

Website | Documentation


Mage aims to empower data teams by effortlessly integrating and synchronizing data from external sources, building real-time and batch pipelines using Python, SQL, and R, and efficiently operating, monitoring, and orchestrating multiple pipelines. The platform delivers a user-friendly developer experience, enabling users to work either locally or in the cloud using Terraform and offering a variety of programming languages for increased versatility. Mage adopts engineering best practices, showcasing modular code with data validations, replacing traditional DAGs with less convoluted code.

Mage's preview feature provides instant feedback through an interactive notebook UI, treating data as a high-priority component by versioning, partitioning, and cataloging data generated within the pipeline. The platform also supports cloud-based collaborative development, Git-based version control, and testing without requiring shared staging environments. Deployment to AWS, GCP, or Azure is simplified with well-maintained Terraform templates, and scaling is facilitated through in-data warehouse transformations or native Spark integration. In the end, Mage offers integrated monitoring, alerting, and observability via an easy-to-use interface, making it straightforward for smaller teams to manage and scale thousands of pipelines, positioning Mage as an excellent Dagster alternative.

Check out Mage:

Website | Documentation

source: https://github.com/mage-ai/mage-ai/blob/master/media/data-pipeline-overview.jpg


Luigi is an influential Python package designed to help developers automate complex data flows with a Python-centric approach. This package provides a well-organized framework for creating and managing data processing pipelines, making it easy to integrate various tasks like Hive queries, Hadoop jobs, and Spark jobs into a single pipeline. It is best suited for backend developers in need of a dependable and expandable batch processing solution for automating intricate data processing tasks.

While Luigi boasts a robust architecture and simplifies restarting failed pipelines, it does come with some limitations. Establishing task dependencies can be challenging, and the package does not support distributed execution, making it more appropriate for small to medium-sized data tasks. Moreover, Luigi's compatibility with specific features is limited to Unix systems, and it does not support real-time or event-triggered workflows, relying on cron jobs for scheduling purposes. Despite these drawbacks, Luigi remains a valuable tool for managing and automating data processing tasks, making it a favored alternative to Dagster among data engineering teams.

Check out Luigi:

Website | Documentation

Source: https://luigi.readthedocs.io/en/stable/


Prefect has become a favored data flow automation platform among data engineers, thanks to its seamless orchestration layer that streamlines the current data stack. By doing away with negative engineering, Prefect empowers data professionals and scientists to more effectively manage their workflows and data pipelines. The Orion engine allows for Python code orchestration, while the user interface provides notifications, scheduling, and run history. Additionally, Prefect facilitates parallelization and scaling via Kubernetes and event-driven workflows, offering cloud-like convenience and on-premises security.

Although Prefect is an ideal choice for enterprise users in search of a managed workflow orchestrator, it does have some drawbacks. The limited free tier may not cater to everyone's needs, and deploying the self-service solution could prove difficult for some. Nevertheless, Prefect remains a top choice for those seeking a more expensive, managed workflow orchestrator. Bolstered by its strong community of engineers and data scientists, Prefect has earned a solid reputation as a trustworthy and widely-used alternative to Dagster within the data engineering realm.

Check out Prefect:

Website | Documentation

Source: https://docs.prefect.io/ui/overview/


Shipyard is an exceptional tool for small data teams seeking to develop innovative business solutions. Its features encompass organizational projects, integrated notifications and error-handling, automated scheduling, on-demand triggers, and a lack of proprietary code configuration. Shipyard delivers a comprehensive and intuitive environment for the efficient creation and implementation of advanced business solutions.

The platform's user-friendly interface facilitates effortless management and offers extensive admin controls and permissions. Moreover, its shareable and reusable blueprints, scalable isolated resources for each solution, and detailed historical logging encourage seamless collaboration and efficient resource management. Shipyard also includes over 100 native open-source Blueprints for tools in your data stack. Utilizing Shipyard, businesses can swiftly and easily deploy, test, and monitor their projects, making it a prime choice for organizations aiming to optimize their workflow processes.

Check out Shipyard:

Website | Documentation

As evident, numerous alternatives to Prefect are available to choose from. Although we may be somewhat partial at Shipyard, we believe you'll appreciate our platform due to its user-friendly nature and swift setup capabilities. If you're interested in discussing orchestration for your organization, don't hesitate to schedule a meeting with our team. We're eager to explore how orchestration can benefit your data team.