Apache Airflow vs. Astro by Astronomer vs. Oracle Warehouse Builder

Airflow is well-suited for data engineering pipelines, creating scheduled workflows, and working with various data sources. You can implement almost any kind of DAG for any use case using the different operators or enforce your operator using the Python operator with ease. The MLOps feature of Airflow can be enhanced to match MLFlow-like features, making Airflow the go-to solution for all workloads, from data science to data engineering.

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Astronomer is well suited for workflow and dependency management for enterprise-level data lakes. It is not a product for data processing though. Different source systems can be integrated, it also provides powerful interfaces for alerting and monitoring. Easy to build DAGs, graphical UI, API support makes the product more user-friendly as well. Astronomer also does a great job on user training.

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The best place for Oracle Warehouse Builder is at the business IT level. It's not suited for business-level users. They are easy confused. One way to reduce the confusion for the developers is to set up the workspaces based on the requirements that are discovered in design sessions. Once this is complete, the implementation of Oracle Warehouse Builder can take flight and be successful.

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• Apache Airflow is one of the best Orchestration platforms and a go-to scheduler for teams building a data platform or pipelines.
• Apache Airflow supports multiple operators, such as the Databricks, Spark, and Python operators. All of these provide us with functionality to implement any business logic.
• Apache Airflow is highly scalable, and we can run a large number of DAGs with ease. It provided HA and replication for workers. Maintaining airflow deployments is very easy, even for smaller teams, and we also get lots of metrics for observability.

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• Workflow management
• Wide availability of plugins
• Dependency management on upstream

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• Easy transformation.
• Easy implementation from Oracle to Oracle systems.
• Ease of usage and easy to learn.
• Starting component of metadata management.

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• UI/Dashboard can be updated to be customisable, and jobs summary in groups of errors/failures/success, instead of each job, so that a summary of errors can be used as a starting point for reviewing them.
• Navigation - It's a bit dated. Could do with more modern web navigation UX. i.e. sidebars navigation instead of browser back/forward.
• Again core functional reorg in terms of UX. Navigation can be improved for core functions as well, instead of discovery.

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• More language agnostic
• Flexible fork and join capabilities
• Near real time UI updates in case of deployment of enhanced DAGs

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• What I noticed is that sometimes OWB doesn't generate the best SQL in the package especially when there are a high number of source tables in the ETL. It would be nice if ETL developers were allowed to update the generated packages in the database directly.
• Another thing - moving OWB ETLs from one database to another one could be easier - for example it would be nice to just copy the generated packages from one database to the other one without doing the deployment of these ETLs through OWB.

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For its capability to connect with multicloud environments. Access Control management is something that we don't get in all the schedulers and orchestrators. But although it provides so many flexibility and options to due to python , some level of knowledge of python is needed to be able to build workflows.

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Multiple DAGs can be orchestrated simultaneously at varying times, and runs can be reproduced or replicated with relative ease. Overall, utilizing Apache Airflow is easier to use than other solutions now on the market. It is simple to integrate in Apache Airflow, and the workflow can be monitored and scheduling can be done quickly using Apache Airflow. We advocate using this tool for automating the data pipeline or process.

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Astronomer is a fast, secure, scalable workload management solution. It provides world-class user training along with easy to interact support.

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Ab>initio, IBM Datastage 8.0

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• Impact Depends on number of workflows. If there are lot of workflows then it has a better usecase as the implementation is justified as it needs resources , dedicated VMs, Database that has a cost
• Donot use it if you have very less usecases

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• It helps to build scalable, available and low maintenance workloads
• Integrated Alerts and notifications helps to detect load issues in the early stages
• Ensures meeting data SLAs

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• It improved understanding of ETL functions. Data is consistent. The speed is pretty good.

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