Building a Data Lakehouse with DuckDB and DuckLake
Sure, there were other options like the mainframe systems from companies such as ICL and IBM, but they were very costly and locked you in to a specific manufacturer. The next big advance in data storage was the data warehouse. This brought information from separate operational systems into a central repository designed specifically for reporting and historical analysis.
- ▪Sure, there were other options like the mainframe systems from companies such as ICL and IBM, but they were very costly and locked you in to a specific manufacturer.
- ▪The next big advance in data storage was the data warehouse.
- ▪This brought information from separate operational systems into a central repository designed specifically for reporting and historical analysis.
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| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/building-a-data-lakehouse-with-duckdb-and-ducklake/ |
| Publication time | Thu, 17 Sep 2026 15:30:02 GMT |
| Retrieval time | 2026-09-17T15:33:44.283Z |
| Last seen | 2026-09-17T15:33:44.283Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | ol-xbzUHXlKI · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Data EngineeringBuilding a Data Lakehouse with DuckDB and DuckLakeStarting with a local Parquet file, then joining it to data stored in the cloudThomas ReidSeptember 17, 202619 min readImage by AIMany years ago, if you wanted to store large amounts of data that could be sensibly queried, a database like Oracle or Postgres and such was your main choice. Sure, there were other options like the mainframe systems from companies such as ICL and IBM, but they were very costly and locked you in to a specific manufacturer. The next big advance in data storage was the data warehouse. This brought information from separate operational systems into a central repository designed specifically for reporting and historical analysis.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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