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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TeamCity is very quick and straightforward to get up and running. A new server and a handful of agents could be brought online in easily under an hour. The professional tier is completely free, full-featured, and offers a huge amount of growth potential. TeamCity does exceptionally well in a small-scale business or enterprise setting.
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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.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
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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The customization is still fairly complex and is best managed by a dev support team. There is great flexibility, but with flexibility comes responsibility. It isn't always obvious to a developer how to make simple customizations.
Sometimes the process for dealing with errors in the process isn't obvious. Some paths to rerunning steps redo dependencies unnecessarily while other paths that don't are less obvious.
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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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TeamCity runs really well, even when sharing a small instance with other applications. The user interface adequately conveys important information without being overly bloated, and it is snappy. There isn't any significant overhead to build agents or unit test runners that we have measured.
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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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TeamCity is a great on-premise Continuous Integration tool. Visual Studio Team Services (VSTS) is a hosted SAAS application in Microsoft's Cloud. VSTS is a Source Code Repository, Build and Release System, and Agile Project Management Platform - whereas TeamCity is a Build and Release System only. TeamCity's interface is easier to use than VSTS, and neither have a great deployment pipeline solution. But VSTS's natural integration with Microsoft products, Microsoft's Cloud, Integration with Azure Active Directory, and free, private, Source Code repository - offer additional features and capabilities not available with Team City alone.
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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
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TeamCity has greatly improved team efficiency by streamlining our production and pre-production pipelines. We moved to TeamCity after seeing other teams have more success with it than we had with other tools.
TeamCity has helped the reliability of our product by easily allowing us to integrate unit testing, as well as full integration testing. This was not possible with other tools given our corporate firewall.
TeamCity's ability to include Docker containers in the pipeline steps has been crucial in improving our efficiency and reliability.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
