Workflow TOOLS

What Are Workflow Management and Orchestration Tools?

https://understandingdata.com/posts/apache-airflow-vs-prefect/

 

As data pipelines and their various tasks grow in complexity, creating automated workflows that handle tasks and their dependencies eventually becomes necessary. Tasks and dependencies form networks that can be modelled as a directed acrylic graph (DAG). These graphs model tasks and their dependencies, displaying the relationship between variables (often called nodes).

DAG Map

Workflow orchestration tools enable data engineers to define pipelines as DAGs, including dependencies, then enabling them to execute tasks in order. 

Additionally, workflow orchestration tools create progress reports and notifications that enable team members to monitor what’s going on. Workflow orchestration tools connect to a wide range of data sources, e.g. APIs, databases, data warehouses, etc. Some key uses include:

• Monitoring data flow between APIs, warehouses, etc.
• Managing pipelines that change at relatively slow, even intervals
• Extracting batch data from multiple sources
• ML model training
• DevOps tasks, like submitting Spark jobs

The end goal is to create a dependable, repeatable, centralised workflow for orchestrating data pipelines and MLOps-related tasks. 

This is a relatively new category of tools, but there are already quite a few options, including:

• Apache Airflow: Originally developed by Airbnb, Airflow was donated to the Apache Software Foundation project in early 2019. Airflow is written in Python and is probably the go-to workflow orchestration tool with its easy-to-use UI. 
• Luigi: Luigi is a Python package for building data orchestration and workflows. It’s simpler for Python users than Airflow overall. 
• Dagster: Dagster is more similar to Prefect than Airflow, working via graphs of metadata-rich, functions called ops, connected by gradually typed dependencies. 
• Prefect: Prefect has become a key competitor to Airflow, but provides a cloud offering with hybrid architecture.
• KubeFlow: For Kubernetes users that want to define tasks with Python. 
• MLFlow: Orchestration specifically for ML projects. 

Here, we’ll be comparing Airflow and Prefect.

 

Orchestration Showdown: Dagster vs Prefect vs Airflow

https://www.zenml.io/blog/orchestration-showdown-dagster-vs-prefect-vs-airflow

In summary, when comparing Airflow, Dagster, and Prefect, each tool brings something unique to the table in data orchestration. If your organization deals with complex, static workflows and models in production, Airflow’s robust framework and strong community support might be your go-to. For those focused on machine learning pipelines and model management, Dagster shines with its local development and testing capabilities. If you’re looking for a tool that excels in cloud-native workflows and dynamic processes, Prefect might just be the perfect fit, offering a scalable solution for your model deployment and monitoring needs.

As data engineering continues to evolve, adopting the right tools is key to staying ahead in managing your workflows efficiently. I hope this exploration has provided you with valuable insights into the latest orchestration tools.

 

Airflow vs Dagster vs Prefect: A Detailed Comparison

https://risingwave.com/blog/airflow-vs-dagster-vs-prefect-a-detailed-comparison/

Use Cases and Recommendations

 

As data engineering landscapes evolve, selecting the most suitable orchestration tool becomes paramount to ensure seamless workflow management. Understanding the best use cases for Airflow, Dagster, and Prefect can guide organizations in making informed decisions aligned with their specific requirements.

 

Best Use Cases for Airflow

 

Complex and Static Workflows

 

For organizations dealing with intricate and static workflows that require meticulous planning and execution, Airflowemerges as a robust solution. Its Directed Acyclic Graphs (DAGs) enable the definition of complex dependencies and task relationships, ideal for scenarios where workflows follow a predetermined sequence.

 

Extensive Community Support

 

The extensive community backing behind Airflow serves as a valuable resource for users navigating challenges or seeking innovative solutions. With active contributors worldwide, Airflow's community ensures continuous development and enhancement of the tool's capabilities, making it a reliable choice for organizations seeking comprehensive support.

 

Best Use Cases for Dagster

 

Local Development and Testing

 

Dagster shines in environments that prioritize local development, testing, and debugging processes. Its focus on enhancing developer productivity through streamlined workflows makes it an ideal choice for teams looking to optimize their software development life cycle locally before deployment.

 

Machine Learning Pipelines

 

Organizations delving into machine learning initiatives can benefit from leveraging Dagster for orchestrating their pipelines. The scalability and flexibility offered by Dagster empower data engineers to manage complex machine learning workflows efficiently while ensuring reliability throughout the process.

 

Best Use Cases for Prefect

 

Cloud-Native Workflows

 

For businesses operating in cloud-centric environments such as AWS or Google Cloud, Prefect proves to be a strategic choice. Its seamless integration with cloud platforms enables scalability tailored to modern cloud infrastructures, making it an optimal solution for organizations embracing cloud-native technologies.

 

Dynamic and Simple Workflows

 

Prefect excels in managing dynamic workflows with changing requirements while maintaining simplicity in orchestration processes. Organizations seeking agility in handling evolving data processes can leverage Prefect's lightweight yet powerful features to streamline their workflow management effectively.