![]() As you say, data engineering is part of data science skills, you are "junior" if you cannot write reproducible code.īeing hard to execute and replicate is theoretically easy to fix. However, there is no excuse for writing bad code and then expecting others to fix. ), but I'm afraid you cannot educate non-engineers in a single day workshop. common code formatter, meaningful variables names, short functions, no hard-coded values. ![]() You mention some issues: non-obvious to understand code and hard to execute and replicate.īad code which is not following engineering best practices (ideas from SOLID etc.) does not get better if you force the author to introduce certain classes. Otherwise, you would be adding hassle with some - bluntly speaking - opinionated and inflexible boilerplate code which not many people will like using. It is suitable for those looking for a more customizable solution that can adapt to various infrastructure and service requirements.I'd focus more on understanding the issues in depth, before jumping to a solution. Apache Airflow, on the other hand, provides more deployment flexibility, a code-based workflow definition using DAGs, and a broader range of integrations beyond AWS. It provides simplicity in deployment and is well-suited for those primarily operating within the AWS ecosystem. In summary, AWS Step Functions is a fully managed, serverless service that offers a visual workflow designer and seamless integration with AWS services. Apache Airflow also provides monitoring and logging features but may require more manual configuration and customization based on specific requirements. It offers comprehensive tracking of workflow progress, capturing execution data, and allowing you to set up alarms for critical events. Monitoring and Logging: AWS Step Functions provides built-in monitoring and logging capabilities. It offers a rich library of operators and hooks, enabling connectivity with diverse services and platforms, both within and outside of the AWS environment. On the other hand, Apache Airflow provides a broader range of integrations beyond AWS. Integration with Services: AWS Step Functions seamlessly integrates with multiple AWS services, including Lambda, Batch, and ECS, enabling effortless incorporation of various AWS offerings into your workflows. DAGs represent tasks and their dependencies in a code-based format, providing a more programmatic way of defining workflows. In contrast, Apache Airflow employs Directed Acyclic Graphs (DAGs) to define workflows. It provides a visual interface where you can design workflows using states and transitions, allowing for a graphical representation of the workflow structure. Workflow Definition: AWS Step Functions uses a state machine-based approach to define and manage workflows. On the other hand, Apache Airflow can be deployed on-premises, in the cloud, or in a hybrid environment, providing you with more deployment flexibility. It follows a serverless architecture, where you don't have to worry about infrastructure management, scaling, or maintenance. Here are the key differences between AWS Step Functions and Apache Airflow:Īrchitecture and Deployment: AWS Step Functions is a fully managed service provided by Amazon Web Services (AWS) that operates in the cloud. Airflow vs AWS Step Functions: What are the differences?ĪWS Step Functions and Apache Airflow are both popular workflow management tools used in the field of data engineering and automation.
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