Second Brain: Crafted, Curated, Connected, Compounded on 10月02日 21:25
ELTP模型:扩展ELT以适应现代AI与数据分析
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本文探讨了ELTP模型,它扩展了ELT(Extract, Load, Transform)的概念,通过增加数据发布步骤,提高了数据在下游消费者和应用程序中的效率。文章强调ELTP模型不仅仅局限于传统的Reverse ETL,而是一个更全面的数据发布框架。

Alternative to ETL and Reverse ETL. More marketing than reality?

In full, the ELTP model encompasses the following steps: 

    Extract data from raw data sources.Load data into durable storage, such as a data warehouse or data lake.Transform raw data into new datasets.Publish data to downstream users and business applications.

This design preserves the benefits of ELT while also giving a framework for ensuring that data is efficiently published to downstream consumers and applications.

# Publish Destinations are more than Reverse ETL

All Reverse ETL destinations are Publish-type destinations, but not all Publish destinations are Reverse ETL. The term “Reverse ETL” can still be useful when communicating the unique challenges of publishing to a SaaS system like Salesforce and Hubspot, but the term falls down when describing a publish flow to a partner’s SFTP site or to an external database. Whether publishing to Salesforce, SFTP, S3, or SharePoint, the Publish framing offers a more robust and holistic architectural model that is not tied to the “pickiness” of API-type destinations.

Read more on ELTP: Extending ELT for Modern AI and Analytics | Airbyte.


Origin: ELTP: Extending ELT for Modern AI and Analytics | Airbyte
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Created 2023-11-07

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ELT ELTP 数据发布 现代AI 数据分析
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