{"id":561,"date":"2026-10-10T13:21:54","date_gmt":"2026-10-10T12:21:54","guid":{"rendered":"https:\/\/florian-wenzel.net\/?post_type=portfolio&#038;p=561"},"modified":"2026-10-10T13:22:22","modified_gmt":"2026-10-10T12:22:22","slug":"why-the-technical-details-still-matter-for-data-engineers","status":"publish","type":"portfolio","link":"https:\/\/florian-wenzel.net\/?portfolio=why-the-technical-details-still-matter-for-data-engineers","title":{"rendered":"Why the Technical Details Still Matter for Data Engineers"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">2026 Youtube Live Event with Andreas Kretz<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Most data platforms today want you to stop thinking about what happens under the hood. Let the platform handle it, click the button, move on. That works great until you hit the case where the details decide everything. In this livestream Andreas Kretz is sitting down with Florian Wenzel (VP Product Management) and J\u00fcrgen Albertsen (Technology and Product Leader) from Exasol to dig into exactly that: the design decisions behind a fast analytical database, and why understanding them still makes you a better data engineer. Here&#8217;s what we&#8217;ll get into:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Under the hood:\u00a0distribution keys, in-memory with disk underneath, cluster scaling and self-tuning, plus my own benchmark numbers from the lab<\/li>\n\n\n\n<li>Python, ML and LLMs inside the database:\u00a0what it&#8217;s great for and where it isn&#8217;t<\/li>\n\n\n\n<li>Conversational BI and MCP:\u00a0the current state and lessons from Exasol&#8217;s experimental server<\/li>\n\n\n\n<li>Exasol Personal Local:\u00a0now on all platforms, new deployment options and the path to Enterprise<\/li>\n\n\n\n<li>Where Exasol fits:\u00a0edge cases beyond Databricks, Snowflake or ClickHouse, and running it alongside your lakehouse<\/li>\n\n\n\n<li>The agentic analytical hub:\u00a0pulling data from many sources and working on it in one place<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe title=\"Why the Technical Details Still Matter for Data Engineers\" width=\"900\" height=\"506\" src=\"https:\/\/www.youtube.com\/embed\/rll4TtEw5-Y?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n","protected":false},"featured_media":0,"menu_order":2026,"template":"","portfolio_categories":[40],"portfolio_tags":[],"class_list":["post-561","portfolio","type-portfolio","status-publish","hentry","portfolio_categories-podcasts"],"acf":[],"_links":{"self":[{"href":"https:\/\/florian-wenzel.net\/index.php?rest_route=\/wp\/v2\/portfolio\/561","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/florian-wenzel.net\/index.php?rest_route=\/wp\/v2\/portfolio"}],"about":[{"href":"https:\/\/florian-wenzel.net\/index.php?rest_route=\/wp\/v2\/types\/portfolio"}],"version-history":[{"count":1,"href":"https:\/\/florian-wenzel.net\/index.php?rest_route=\/wp\/v2\/portfolio\/561\/revisions"}],"predecessor-version":[{"id":562,"href":"https:\/\/florian-wenzel.net\/index.php?rest_route=\/wp\/v2\/portfolio\/561\/revisions\/562"}],"wp:attachment":[{"href":"https:\/\/florian-wenzel.net\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=561"}],"wp:term":[{"taxonomy":"portfolio_categories","embeddable":true,"href":"https:\/\/florian-wenzel.net\/index.php?rest_route=%2Fwp%2Fv2%2Fportfolio_categories&post=561"},{"taxonomy":"portfolio_tags","embeddable":true,"href":"https:\/\/florian-wenzel.net\/index.php?rest_route=%2Fwp%2Fv2%2Fportfolio_tags&post=561"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}