Beyond Apache Airflow: Discovering 8 Best Alternatives - AITechTrend

Beyond Apache Airflow: Discovering 8 Best Alternatives

Workflow software refers to tools or platforms that automate, streamline, and manage the sequence of tasks, processes, and operations within an organisation. These software solutions usually include tools for developing, executing, and monitoring processes, allowing users to improve productivity, ensure consistency, and collaborate across teams. Workflow software frequently includes visual interfaces for developing workflows, connection with other systems and applications, and reporting and analytics features to track progress and suggest areas for improvement. Overall, workflow software seeks to simplify complex company processes while enabling more efficient and transparent operations.

Apache Airflow is a platform that is created by the community to programmatically author, schedule and monitor workflows. It has features like pure python, useful UI, robust integrations, easy to use, open source and so on. To know more about the Apache Airflow you can refer to this YouTube video: . In this article, we’ll delve into eight robust alternatives to Apache Airflow, each offering unique features and advantages for different use cases. These alternatives provide diverse solutions for workflow management, catering to the varying needs of modern businesses.

  • Alternatives of Apache Airflow
  1. Luigi
  2. Prefect
  3. Dagster
  4. Apache NiFi
  5. AWS Step Functions
  6. Kedro
  7. Pinball
  8. Jenkins
  1. Luigi

Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualisation etc. It also comes with Hadoop support built in. The purpose of Luigi is to address all the plumbing typically associated with long-running batch processes. 

Luigi provides an infrastructure that powers all kinds of stuff including recommendations, toplists, A/B test analysis, external reports, internal dashboards, etc. Luigi was built at Spotify, mainly by Erik Bernhardsson and Elias Freider. 

According to a survey conducted by Data Council, 32% of respondents preferred Luigi for workflow orchestration in data pipelines. Luigi includes built-in support for a variety of task kinds and works well with other Python tools and frameworks. Visualisation provides insights into pipeline execution, enabling effective monitoring and debugging.

  1. Prefect

Prefect helps data engineers and AI engineers deploy code that’s easy to trust. They build lightweight developer experiences for interacting with complex systems like data and AI stacks. Prefect just released interactive workflows, incidents, new compute options and much more. 

It provides features like control panel, pure python, recover quickly, easy local development and choose your own infrastructure. Prefect’s unique feature is its support for dynamic process execution, which allows customers to build flexible and adaptable pipelines. It also has advanced scheduling features and seamless connection with cloud platforms, making it perfect for distributed and scalable operations.

A survey by the State of Data Science and Machine Learning revealed that 18% of respondents preferred Prefect over other workflow orchestration tools.

  1. Dagster

Dagster is a next-generation open source orchestration platform for the development, production, and observation of data assets. It has features like a productive platform, robust orchestration engine and unified control panel. 

Dagster’s main abstraction, called “solids,” encapsulates data processing logic and allows for dependency-based execution. Its robust type system and tight integration with Python libraries allow for smooth interaction with external systems and services. 

Dagster’s GitHub repository has witnessed steady growth, with over 3,000 stars and active contributions from the community.

  1. Apache NiFi

Apache NiFi is a data flow management system that enables real-time data intake, processing, and routing. It provides a graphical user interface for developing and monitoring data flows, making it accessible to people with a variety of technical backgrounds. 

NiFi’s extensible architecture and comprehensive processing library allow for smooth integration with a wide range of data sources and sinks. Its built-in security measures and data provenance monitoring enable comprehensive data governance and compliance. According to the Apache Software Foundation, NiFi has been downloaded over 5 million times and is deployed in numerous production environments worldwide.

  1. AWS Step Functions

AWS Step Functions is a fully managed workflow orchestration service offered by Amazon Web Services. It allows customers to synchronize AWS services, serverless tasks, and custom actions across dispersed applications. With Step Functions’ visual workflow editor, users can construct and visualize complex workflows with ease using drag-and-drop. 

It includes error handling, retries, and state management features to ensure that business operations run smoothly. According to AWS, Step Functions has experienced significant adoption, with thousands of customers leveraging its capabilities for various use cases, including application orchestration, data processing, and workflow automation.

  1. Kedro

Kedro is an open-source workflow development platform that automates the creation, administration, and deployment of data pipelines. Kedro, built on top of familiar Python tools such as pandas, scikit-learn, and Apache Spark, provides a modular and reproducible approach to data pipeline development, making it a popular choice among data engineers and data scientists

Kedro allows users to define their processes using common Python capabilities, assuring flexibility and ease of use. Its straightforward project structure and rich documentation allow for speedy onboarding and seamless communication among team members. Kedro’s interface with prominent data science tools and cloud platforms adds to its adaptability and scalability, allowing users to deploy pipelines across several settings with ease. 

A survey done by the State of Data Science and Machine Learning found that 25% of respondents favored Kedro for workflow orchestration and pipeline construction. Furthermore, Kedro’s GitHub repository has received over 2,000 stars and active contributions from the community, demonstrating that it is becoming increasingly popular and widely adopted in the data engineering ecosystem.

  1. Pinball (GitHub)

Pinball is an open-source workflow management solution created by Pinterest to simplify and automate large data pipelines. Unlike typical workflow solutions, Pinball employs basic Python programming conventions, making it straightforward for developers to build and orchestrate processes using Python code. With Pinball, users may develop dynamic workflows by leveraging Python’s large ecosystem of tools and frameworks. Its flexible scheduling capabilities enable for the execution of tasks at particular intervals or in response to external events. Pinball also provides built-in support for dependency management, ensuring that jobs are done in the correct order.

One of Pinball’s important advantages is its interaction with Pinterest’s workflow orchestration system, which allows for smooth scalability and stability. Organizations can now easily manage large-scale data processing pipelines. According to GitHub data, the Pinball repository has received over 2,000 stars and several contributions from the open-source community. This active interaction indicates Pinball’s growing popularity and people’s strust in its powers.

  1. Jenkins

Jenkins is a popular open-source automation server that supports continuous integration and delivery (CI/CD) workflows. Jenkins’ straightforward UI and vast plugin ecosystem allow developers to automate a variety of processes, including building, testing, and deploying software applications. One of Jenkins’ primary assets is its ability to adapt to various development environments and workflows. Jenkins provides configurable solutions to satisfy the demands of small teams working on a single project as well as large organisations managing complicated development pipelines.

According to recent statistics, Jenkins is one of the most used CI/CD technologies in the business. According to a Stack Overflow study, Jenkins is one of the top three most popular CI/CD technologies among developers throughout the world. Jenkins also has a thriving community of users and creators, with thousands of plugins available to extend its capabilities and interact with numerous tools and services.

Workflow management systems are diverse, with each offering unique features and benefits. Whether you want simplicity, scalability, or specialized capabilities, there is an option to Apache Airflow that will match your needs. Organizations can select the best solution for their process orchestration and automation needs by investigating these eight options. The workflow management system you choose should be appropriate for your specific use cases, technological needs, and organizational goals. By comparing each alternative’s features, capabilities, and community support, you can make an informed decision that promotes workflow efficiency and innovation.

The statistics provided in this article are based on industry surveys, GitHub repository analytics, and vendor-reported data as of the publication date. Actual numbers may vary over time.