Apache Airflow is an open source tool that can be used to programmatically author, schedule and monitor data pipelines using Python and SQL.
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Astro by Astronomer
Score 9.0 out of 10
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For data teams looking to increase the availability of trusted data, Astronomer provides Astro, a data orchestration platform, powered by Airflow. Astro enables data engineers, data scientists, and data analysts to build, run, and observe pipelines-as-code. Astronomer is the driving force behind Apache Airflow™, the de facto standard for expressing data flows as code. Airflow is downloaded more than 8 million times each month and is used by hundreds of thousands of teams around the world.
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Microsoft Defender for Cloud
Score 8.5 out of 10
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Microsoft Defender for Cloud is a Cloud Security Posture Management (CSPM) and Cloud Workload Protection Platform (CWPP) for Azure, on-premises, and multicloud (Amazon AWS and Google GCP) resources.
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.
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.
Microsoft is well-suited with its definitive cloud, and I also like its Microsoft Intune ID. The conditional policies are great with that, and they're really good and well situated, so you can't beat them at that conditional policy level. Less appropriate, as I said, some of these low-hanging fruit features, like being good in phishing campaigns, and then I feel like maybe doing better at their seam products. So we'll see how that goes.
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.
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.
Granular permissions and role-based access management could improve security. This would enable organizations to control who has access to and can set specific features.
While it offers integration with various Microsoft services, expanding support for third-party cloud platforms and applications would enhance its versatility. Many organizations use multiple cloud providers, and broader compatibility would be advantageous.
The cost structure could be more transparent, especially for larger organizations with extensive cloud resources. Clearer cost breakdowns and predictions would help organizations budget more effectively.
It is a great product that integrates nicely when running an Azure platform and even multi-cloud environment. Not looking for point-solutions but a suite that answers most requirements. It is very comfortable being able to use KQL, workbooks and automation that is native to the azure platform
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.
My visibility is limited because I'm only doing very small pieces of what the overall org does. And also, we have limitations on what we're allowed to use. It's not like we get a new product as users or leadership level users, and everything is on, and we can just do whatever we want. We're very restricted in what we can use any tooling within the org because of the different levels of regulatory constraints we have, because of just the nature of who we are inherently. So that's why. I don't think it's necessarily the product. I think it's more or less of what we're able to do with the product.
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.
Microsoft Defender for Cloud is definitely the choice with the latest market trend and attacks that are currently happening. Microsoft has been able to safe guard a lot after the recent serious attacks happening globally in the digital world. There is a trust in this software and with the latest updates and machine learning capabilities, Microsoft Defender for Cloud should be the choice.
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
It simplifies security management and saves time. I'm not sure, but I'm very confident it saved me a couple of paychecks by centralizing the data I need to secure the cloud environment.
I also utilize the inventory overview to monitor my team's activities and verify they are following internal regulations, as well as cost overruns.
The recommendations can be utilized as a valuable instructional tool. I have the team explain why they are receiving them, why they are not following them, and what they are doing differently.
