Running Spark 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.

Spark

Apache Spark is an open-source data processing engine that is designed to improve data-intensive applications' performance. It provides a more efficient way to process data, which can be used to speed up the execution of data-intensive tasks. It lets you process big data sets faster by splitting the work up into chunks and assigning those chunks across computational resources. Spark has been called a "general-purpose distributed data processing engine" and "a lightning-fast unified analytics engine for big data and machine learning". It provides development APIs in Java, Scala, Python, and R, and supports code reuse across multiple workloads, batch processing, interactive SQL queries, streaming analytics, machine learning, and graph processing. Spark is especially used to access and analyze social media profiles, call recordings, emails, etc. This helps companies make correct business decisions for target advertising, customer retention, fraud detection, etc.
Open source orchestrators like Airflow are one of the primary means by which companies leverage Spark 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 Spark 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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