Although Astronomer is a widely-used solution for handling data workflows, it's not without its drawbacks, prompting people to look for other options. Both tech-savvy individuals and business stakeholders often cite issues such as inadequate documentation, a steep learning curve, and complicated production deployment and upkeep. In the following article, we'll delve into alternatives and competitors to Astronomer that mitigate these issues, offering a more accessible and streamlined approach to workflow management.
Shipyard
Indeed, we're starting our discussion of alternatives to Airflow by spotlighting Shipyard. While we're naturally biased, we believe that Shipyard stands as a stark contrast to Airflow. Shipyard is designed to be user-friendly, quick to set up, and accommodates individuals from various technical backgrounds. Plus, it's not restricted to Python—you can also run your Shipyard workflows locally for testing and deployment.
When it comes to value for your investment, Shipyard offers the most comprehensive and versatile data orchestration features. Highlights include project organization capabilities, integrated notifications and error management, automated scheduling, and trigger options that don't require proprietary code. Additionally, the platform offers shareable blueprints, dedicated scaling resources for each solution, and extensive historical logs, all of which contribute to smooth teamwork and effective resource utilization.
You have the flexibility to either opt for our ready-made blueprints, modify the code to suit your needs, or code from scratch in Shipyard using Python, Bash, or Node.
Rounding out these capabilities is an intuitive user interface for straightforward management, along with robust admin controls and permissions. This ensures that users can exercise a high degree of oversight over their projects.
Check out Shipyard:
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Mage
Mage delivers a simplified experience for developers, allowing them to work either locally or in the cloud via Terraform, and offering a selection of programming languages for greater adaptability. Instead of traditional DAGs filled with spaghetti code, Mage adheres to engineering best practices by utilizing modular code and incorporating data validations.
The platform's preview functionality offers real-time feedback through an interactive notebook-style user interface. It elevates the importance of data by implementing versioning, partitioning, and cataloging for data generated within the pipeline. Mage also facilitates team collaboration in the cloud, integrates seamlessly with Git for version control, and allows for testing without the need to wait for communal staging environments.
Additionally, Mage comes equipped with built-in features for monitoring, alerts, and observability, all accessible via an easy-to-navigate interface. This makes it straightforward for smaller teams to oversee and scale multiple pipelines with ease.
Luigi
Luigi is a Python library tailored for extended batch processing, offering a structure for building and overseeing data processing workflows. Created by Spotify, Luigi facilitates the automated running of data tasks across multiple objects in a batch setting. Its core attributes encompass modularity, expandability, scalability, and a technology-neutral approach. The package is capable of integrating diverse tasks like Hive queries, Hadoop jobs, and Spark jobs into a unified workflow. It is particularly well-suited for backend developers seeking to automate intricate data flows through a Python-compatible solution.
Although Luigi offers a user-friendly architecture and makes it easier to restart failed workflows, it comes with certain drawbacks. Crafting task dependencies can prove to be a hurdle, and the absence of distributed execution features means it's better suited for small to medium-sized data tasks. Additionally, some functionalities are restricted to Unix-based systems, and it doesn't accommodate real-time or event-driven workflows, relying instead on cron jobs for task scheduling.
Azure Data Factory
Azure Data Factory serves as a fully managed, serverless platform for data integration, tailored for teams looking for a serverless option that integrates seamlessly with Microsoft-centric technologies like Azure Blob Storage and Microsoft SQL Server. The service prioritizes no-code elements for pipelines, allowing for the migration of SQL Server Integration Services (SSIS) and the creation of ETL/ELT pipelines. It comes with integrated Git and CI/CD capabilities, eliminating the need for manual coding. Operating on a pay-as-you-use basis, this fully managed serverless offering scales according to your needs, ensuring both flexibility and cost efficiency.
With over 90 pre-configured connectors for incorporating data from both on-site and SaaS sources, Azure Data Factory enables large-scale orchestration and monitoring. It also has robust ties with the wider Microsoft Azure ecosystem, positioning it as a viable alternative to Airflow for those already invested in Azure services or those who desire smooth interoperability with Microsoft-based solutions.
Dagster
Dagster is engineered for data experts who lean towards a software-centric methodology for managing data pipelines. Its core functionalities encompass a platform for crafting software-defined assets, a sturdy orchestration engine with adaptable architecture, and a consolidated control panel for centralizing metadata. Unlike Apache Airflow, Dagster employs an asset-centric model for orchestration, emphasizing dependencies between data assets.
What sets Dagster apart is its decoupling of IO and resources from the DAG's core logic, making local testing more straightforward compared to Airflow. The platform encourages practices like continuous integration, code assessments, staging areas, and debugging. On the downside, its cloud-based pricing structure is somewhat complex, featuring varying per-minute compute rates. While an open-source variant is accessible on GitHub, it does present a significant learning challenge. Dagster is most appropriate as an alternative to Airflow for those specializing in data engineering.
Prefect
Prefect serves as a platform for automating data flows, enabling users to coordinate Python code through its Orion engine, which accommodates type annotations, asynchronous support, and first-class functions. The user interface of Prefect allows for the configuration of alerts, the scheduling of workflows, and the examination of execution history. Additionally, its open-source software provides layers for orchestration and execution that can be operated independently. The platform comes with a library of ready-made tasks for executing shell scripts, overseeing Kubernetes jobs, and even sending out tweets. A community of engineers and data scientists actively supports Prefect.
The platform also offers features for parallel execution and scalability, leveraging Kubernetes and accommodating event-driven workflows. However, its restricted free tier and the complexities associated with deploying its self-service solution could pose challenges for some users. In a nutshell, Prefect is a fitting choice for enterprise-level users seeking a more expensive but managed alternative to Airflow for workflow orchestration.
Here at Shipyard, we may be a little biased (okay, a lot biased), but we really think you're gonna love our platform. It's super easy to use and you can get up and running in no time. And if you're interested in learning more about how orchestration can help your data team, our team would be more than happy to chat with you or try Shipyard now free - no credit card required.
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