Apache Airflow is an open source tool that can be used to programmatically author, schedule and monitor data pipelines using Python and SQL. Created at Airbnb as an open-source project in 2014, Airflow was brought into the Apache Software Foundation’s Incubator Program 2016 and announced as Top-Level Apache Project in 2019. It is used as a data orchestration solution, with over 140 integrations and community support.
N/A
HashiCorp Nomad
Score8 out of 10
N/A
Nomad, from HashiCorp, is presented as a simple, flexible, and production-grade workload orchestrator that enables organizations to deploy, manage, and scale any application, containerized, legacy or batch jobs, across multiple regions, on private and public clouds. Nomad's workload support enables an organization to run containerized, non containerized, and batch applications through a single workflow. Nomad is available open source, or via a supported enterprise plan.
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.
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
Nomad is well suited for organizations who wish to tackle the problem of cloud computing with as little opinion as possible. Where competing tools like Kubernetes limit the concept of "batteries included," Nomad relies on engineers understanding the missing components and filling them in as necessary. The benefit of Nomad is the ability to build a system out of small pieces with the cost of having more complexity at a system level compared to alternatives.
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
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.
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
Nomad only handles one part of a full platform. Expertise and vision are required in implementing an entire system that is functional enough for an organization to rely on. This includes other tools to handle things like secrets, service discovery, network routing, etc.
Nomad is delayed in some modern functionality, like features for service-mesh and open tracing. These features are on the tool's roadmap, but there's currently no native support. These paradigms can be established still, but require more expertise outside of Nomad itself.
Nomad is not the leading tool for this space, and as such risks being left behind by tools with much greater support, such as Kubernetes.
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
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.
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
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.
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
Nomad's primary competitor is Kubernetes, specifically its scheduling component. Kubernetes is a much more complete system that will handle more things than job scheduling, including service discovery, secrets management, and service routing. There also exists a much larger community support for Kubernetes vs Nomad. One might say Kubernetes is the safer choice between the two. Kubernetes is the complete "operating system" for cloud computing, but with it includes complexities that are "Kubernetes" specific. The decision really comes down to a mindset of monolith vs components. With Kubernetes, I would argue you choose the entire system as a whole. With Nomad, you design your system piece by piece. There is no wrong answer.
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
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
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
Nomad has allowed our organization to deploy quicker and more frequently with a lower failure rate.
Nomad has brought in consistency from an operations perspective.
Nomad's performance allows us to scale infinitely while providing functionality that reduces mean time to repair (canary deploys, versioning, rollbacks, etc).
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
