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Covers Airflow features like Dynamic Task Mapping, task-group modularization, data-driven scheduling, and storage, plus guidance on integrations with providers and tools for warehouses and data lakes.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/apache-airflow-best-practices-etl-and-elt-pipelines/672887/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/apache-airflow-best-practices-etl-and-elt-pipelines/672887.png","ImageObject",300,407,{"name":92,"@type":93},"Ben ","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-11-02",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main difference between ETL and ELT in a data pipeline?","Question",{"text":112,"@type":113},"ETL performs transformations before loading, formatting and processing data “in-flight” before it reaches the target storage. 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Whether you’re starting fresh or enhancing an existing setup, it covers everything from conceptual overviews and pipeline planning to essential features and integrations.  \nAt Astronomer, we hope this eBook serves as a valuable resource, and we can’t wait to see what you’ll build with it. \u003C3  \nhelpful hints  \nIf you have never used Airflow before, we recommend you start with our Get Started tutorial before reading this eBook.  \nComplete the Tutorial  \nSee the companion GitHub repository for example code showcasing the patterns mentioned here.  \nCheck out the Repo  \nTo try out the concepts explained in this eBook ina cloud-environment, start a free 14-day Astro trial.  \nStart for Free  \nApache Airflow® Best Practices: ETL and ELT Pipelines  \n3  \nDefinitions  \nBefore you dive into writing code, it is important to define the patterns used, as well as talk about how they are implemented in real-life scenarios.  \nWhat is ETL?  \nETL stands for Extract (E), Transform (T), Load (L) . It’s the original pattern designed to move data from a source system, transform it, and load it into a target system, commonly a data warehouse or data lake. The defining feature of ETL is that the transformation happens “in-flight,” meaning data is processed and formatted before being loaded into the target system. This allows for control over which parts of th","cbCaibbSdEo61go1","https://ap.wps.com/l/cbCaibbSdEo61go1","pdf",14726101,44,"English","# Definitions\n## What is ETL?\n## What is ELT?\n## ETL and ELT in real-world scenarios\n# Pre-flight and design guidance\n## Pre-flight checklist for ETL and ELT pipelines\n## Key decisions for ETL and ELT pipelines\n## ETL vs ELT\n## Choose a method for passing data between tasks\n## Airflow’s compute vs external compute\n## Choice of operators and decorators\n## Data Quality Checks\n# Building with Airflow\n## ETL and ELT DAG writing best practices\n## Scaling\n## Airflow environment best practices\n## Key Airflow features for ETL and ELT\n## Other important features for ETL and ELT","[{\"question\":\"What is the main difference between ETL and ELT in a data pipeline?\",\"answer\":\"ETL performs transformations before loading, formatting and processing data “in-flight” before it reaches the target storage. ELT loads raw data first, then applies transformations inside the target system to produce derived assets.\"},{\"question\":\"What guidance does the document provide before writing Airflow code for ETL/ELT pipelines?\",\"answer\":\"It includes a pre-flight checklist, outlines key decisions, compares ETL vs ELT, and discusses how to pass data between tasks, when to use Airflow compute versus external compute, and which operators or decorators to choose.\"},{\"question\":\"Which Airflow practices and features are emphasized for ETL and ELT DAGs?\",\"answer\":\"The guide highlights core DAG writing best practices such as avoiding top-level code, using automatic retries, applying software development lifecycle practices, and implementing testing and validation. It also covers scaling and features like Dynamic Task Mapping, programmatic DAG generation, modular task groups, data-driven scheduling, and object storage.\"}]","Apache Airflow Best Practices - ETL and ELT Pipelines | PDF",111]