Running Big Data Jobs using Airflow

Airflow

Apache Airflow is an open-source platform for authoring, scheduling and monitoring data and computing workflows. It was first developed by Airbnb and is now under the Apache Software Foundation. Airflow uses Python to create workflows that can be easily scheduled and monitored. Airflow can help you move data from one source to a destination, filter datasets, apply data policies, manipulation, monitoring and even call microservices to trigger database management tasks. It can be used for batch jobs, organizing, monitoring, and executing workflows automatically. Airflow has been used by many companies for various use cases such as ETL pipelines, machine learning workflows, data warehousing, and more.

Big Data

Big data refers to data that is so large, fast or complex that it's difficult or impossible to process using traditional methods. It can be a combination of structured, semi-structured and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling and other advanced analytics applications. Big data technology deals with data storage that has the capability to fetch, store, and manage big data. It allows users to store the data so that it is convenient to access. Big data analytics helps companies leverage their data to identify opportunities for improvement and optimization. Across different business segments, increasing efficiency leads to overall more intelligent operations, higher profits, and satisfied customers.
Open source orchestrators like Airflow are one of the primary means by which companies leverage big data in production. Airflow offers a mechanism to schedule and monitor these jobs as part of more complex workflow graphs. Kaspian has a native operator for Airflow; this operator makes it easy to either swap to or get started with running Big Data jobs that utilize Kaspian's flexible compute layer.
Learn more about Kaspian and see how our flexible compute layer for the modern data cloud is already reshaping the way companies in industries like retail, manufacturing and logistics are thinking about data engineering and analytics.

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