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Apache Airflow, initially created by Airbnb in 2015, is an open-source platform designed to programmatically manage, schedule, and monitor workflows and data pipelines. It enables organizations to define, schedule, and orchestrate complex workflows and data processing pipelines in an efficient and organized manner. Airflow allows users to easily create workflows, monitor pipeline execution, and troubleshoot issues. The platform is built on Python and offers a vast range of connectors for easy integration with various data sources and destinations.
Apache Airflow works by defining workflows using Python code called "DAGs" (Directed Acyclic Graphs). These DAGs are comprised of tasks that make up the workflow, with each task being a distinct unit of work that can be executed independently. Tasks can be run manually or scheduled to run automatically, with Airflow automatically managing dependencies between tasks and monitoring pipeline progress. Airflow comes equipped with a web-based UI that allows users to visualize the status of their workflows, inspect task logs, and manage DAGs, among other functions.
Apache Airflow is essential for organizations that need to process large amounts of data efficiently. It offers several advantages over traditional ETL (extract, transform, load) tools and batch processing systems, including:
Apache Airflow is used in a range of data processing and analytics use cases, including:
Apache Airflow is often used in conjunction with other data processing and analytics technologies, including:
Dremio is an open-source data lakehouse platform that enables users to query data in various data sources, including data lakes, data warehouses, and databases. Apache Airflow can be used in conjunction with Dremio to create and manage workflows that move data between these sources. Dremio users can leverage Airflow to automate data ingestion, processing, and analysis, making it easier to scale data operations and improve overall efficiency. In addition, Airflow allows Dremio users to create complex workflows with dependencies, making it easier to manage and monitor pipeline progress.