Apache Airflow vs. SAP Data Services

For a quick job scanning of status and deep-diving into job issues, details, and flows, AirFlow does a good job. No fuss, no muss. The low learning curve as the UI is very straightforward, and navigating it will be familiar after spending some time using it. Our requirements are pretty simple. Job scheduler, workflows, and monitoring. The jobs we run are >100, but still is a lot to review and troubleshoot when jobs don't run. So when managing large jobs, AirFlow dated UI can be a bit of a drawback.

Incentivized

SAP Data Services is a data integration and transformation software application. It allows users to develop and execute workflows that take data from predefined sources called data stores (applications, Web services, flat-files, databases, etc.)

Incentivized

• 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

• Extracting data from various types of data sources
• Transforming data using joins, functions, look ups, etc.
• Loading data into different kinds of targets

Incentivized

• A local "dry run" or IDE plugin that can validate and simulate DAG execution without needing a full environment.
• Better feedback on DAG parse errors in the UI or CLI.
• Navigating large DAGs with hundreds of tasks can be slow and hard to understand visually.

Incentivized

• New connectivity options for cloud solutions must be added.
• Avoid dependency from flash in Information Steward scorecard.
• More connectivity options for hadoop distributions.

Incentivized

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

As other SAP products, there is excellent support by SAP on this tool. You can access the SAP Help portal in order to give support either from the huge community or directly from SAP.

Incentivized

Apache Airflow is suited for a much wider set of use cases compared to Databricks. You can run it anywhere, and there is also no vendor lock-in. With Airflow, we can utilize almost any compute engine. Same thing we want to do with Databricks. There might be some level of difficulty based on the support.

Incentivized

SAP Data Services is better at integration with other SAP products and data sources with native HANA connectors. There are also accelerators to consider when looking at it as a data migration tool for SAP implementations.

Incentivized

• Most of the ETL processes were automated, cutting down on human labor.
• Apache Airflow's user interface (UI) was very informative and straightforward.
• Since ETL processes were providing data via airflow, we were able to gain a deeper comprehension of the data at hand.

Incentivized

• DS has had a very positive impact by reducing deployment times for SAP and has provided ability to deploy multiple sites at the same time.
• Ongoing master data creation/migration benefits significantly from DS, and it's easy to stand up a new business unit.
• Data accuracy improved and manual inspections are minimal.

Incentivized