> For the complete documentation index, see [llms.txt](https://daton-sarasanalytics.gitbook.io/daton/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://daton-sarasanalytics.gitbook.io/daton/platform/features/data/inserts-updates-and-upserts.md). # Inserts, Updates, and Upserts Daton supports a variety of data destinations and has to ensure that the data loading happens in accordance to the capabilities and limitations of the data destination. This page gives you a high level overview of how data loading behaves for different destinations. ### Oracle Autonomous Data Warehouse Autonomous Data Warehouse is Oracle's flagship data warehousing product. It is based on the tried and tested Oracle database with additional capabilities being added to make the database fully autonomous. Daton leverages the native data loader provided by Oracle ADW to load data extracted from source systems. When these loads happen, the native loader also generates log tables in the warehouse. **<>** ### Google BigQuery Google BigQuery does not support Upserts i.e updates to existing records based on a primary key while doing bulk loads. The tables in BigQuery are append only and as a result users may see duplicate records if data in the source system changes and you have set that data to replicate to the data warehouse. So users of BigQuery will have to use the metadata created by Daton to extract the most recent record for a particular primary key and use that record for any reporting or analysis. **<>** ### AWS Redshift AWS Redshift is Amazon's flagship data warehousing product. It was one of the first generation cloud data warehouses that quickly garnered a lot of popularity. Redshift is based on an underlying PostgreSQL DB and supports a lot of functionality that comes out of the box with PostgreSQL. **<>** --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://daton-sarasanalytics.gitbook.io/daton/platform/features/data/inserts-updates-and-upserts.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.