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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.onehouse.ai
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| | | | | Onehouse Table Optimizer intelligently optimizes your table data layouts and automates away the tedious chore of manually tuning infrastructure and operations. | |
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www.morling.dev
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| | | This page gives an overview over some talks I have done over the last years. I have spoken at large conferences such as QCon San Francisco, Devoxx and JavaOne, local meet-ups as well as company-internal events, covering topics such as Debezium and Change Data Capture, Bean Validation, NoSQL and more. If you'd like to have me as a speaker at your conference or meet-up, please get in touch. 2025 Current (Bengaluru, India): Ins and Outs of the Outbox Pattern | ||
