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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Cribl Stream
Score 5.7 out of 10
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Cribl Stream is a vendor-agnostic observability pipeline used to collect, reduce, enrich, normalize, and route data from any source to any destination within an existing data infrastructure. It is used to achieve full control of an organization's data stream.
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Pricing
Apache Airflow
Cribl Stream
Editions & Modules
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Pricing Offerings
Apache Airflow
Cribl Stream
Free Trial
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Free/Freemium Version
Yes
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Premium Consulting/Integration Services
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Entry-level Setup Fee
No setup fee
No setup fee
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Community Pulse
Apache Airflow
Cribl Stream
Features
Apache Airflow
Cribl Stream
Workload Automation
Comparison of Workload Automation features of Product A and Product B
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.
Advantages - if you'd like to re-shape/manipulate data, Cribl LogStream comes to help! - If you'd like to enrich data within data pipeline without any struggle, Cribl LogStream is the one! - If you'd like to reduce data size, cribl is the one! Disadvantages - there is ML/AI module for streaming data. - There is no sigma integration for security use cases.
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.
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.
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.
-Cribl LogStream has a huge growing community and plugin play packs that help you to onboard and reduce your size within 5 min. -Friendly user interface -The broker feature saves your life against regulations. - field extraction's never been so easy before. - multiple sources and destinations feature to give you an easy playground.
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
