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lakefs.io | ||
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rmoff.net
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| | | | | [AI summary] This article discusses the evolution of data engineering in 2022, focusing on storage and access methods for analytical data, including the transition from traditional data warehouses to modern data lakehouses and open formats. | |
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jack-vanlightly.com
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| | | | | In the previous post, I covered append-only tables, a common table type in analytics used often for ingesting data into a data lake or modeling streams between stream processor jobs. I had promised to cover native support for changelog streams, aka change data capture (CDC), but before I do so, I think we should first look at how the table formats support the ingestion of data with row-level operations (insert, update, delete) rather than query-level operations that are commonly used in SQL batch commands. | |
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www.getorchestra.io
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| | | | | Discover how Data Engineers are using Apache Iceberg in Snowflake to replace External Tables, thereby truly deocupling compute and storage. Find out more. | |
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www.ai21.com
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| | | By grounding AI in an organization's unique expertise, Retrieval-Augmented Generation (RAG) helps enterprises overcome hurdles in deploying large language models. AI21's RAG Engine provides advanced retrieval capabilities without enterprises having to invest heavily in development and maintenance. | ||
