Apache Airflow vs. Oracle Autonomous Transaction Processing

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.

Incentivized

If your company's value is not increased by maintaining databases, you should use the ATP.

• 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.

Incentivized

• Quick and simple to deploy (less than 5 steps to create a new instance)
• No administrative tasks (backup, re-sizing) required

Incentivized

• 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.

Incentivized

• Connectivity between ATP and Oracle Kuberentes Engine cluster seems to drop randomly
• The ATP could come in an even smaller shape as the smallest shape is already quite big . But if must be more than always free, as that version does have connectivity limitations about 20 connections

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.

Incentivized

You get almost all the features of Oracle Database, including the ability to deploy APEX applications, without having to worry about system, storage or database configuration and maintenance, backups, etc. Also, you can start for free and move up adding resources as you need.

Incentivized

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.

Incentivized

Both Azure SQL Database and Oracle Autonomous Transaction Processing offer a powerful and robust database platform. The advantage of Autonomous Transaction Processing is the ability to host APEX applications for free and without any special/complicated deployment or configuration process. Having a rapid development environment embedded for free was the main advantage for choosing Oracle as my cloud database provider.

Incentivized

• 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

Incentivized

• Approx 100 times faster spin up than manual setup of an Oracle DB
• 3h annual maintenance for big version releases (18c to 19c) compared to est. 500h annual maintenance with manual database updates