Using a local infrastructure to work with Big Data is often expensive and inefficient: tasks that take only a few hours a week require huge computing power that must be paid for, configured, and maintained. Therefore, many companies are moving big data processing to the cloud, wherein in a matter of minutes, you can get a fully tuned and optimized data cluster with pay per second – for the resources used.
Another reason why working with BigData is preferable in the cloud is the ability to use Kubernetes aaS. The main advantages of working with Big Data in Kubernetes are flexible scaling and isolation of environments. The first allows you to automatically change the resources allocated in the cloud depending on changing loads. The second ensures the compatibility of different versions of libraries and applications in the cluster through containerization.
Since AirFlow is designed to orchestrate ETL processes in Big Data and Data Science, it can be launched and even recommended in the cloud. AirFlow also works well with Kubernetes. Here are the ways to launch Airflow in Kubernetes were briefly mentioned above – we will describe them in more detail:
Of course, AirFlow is far from the only solution of this kind in the IT market. There are many other tools for planning and monitoring ETL processes, both paid and open-source. You can completely get by with the standard Cron scheduler in the simplest cases, setting up workflows through Crontab.
Here are some typical scenarios where AirFlow might be the best choice:
Also Read: Data Lineage And Provenance: – Big Data Management For Beginners
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