DuckDB Labs released #DuckLake 1.0 - a data lake format that stores table metadata in a SQL database, rather than spreading it across object storage files.
Key features:
• catalog-stored small updates
• improved sorting and partitioning
• compatibility with Iceberg-style data features
Learn more ⇨ https://bit.ly/48PsPIS
Off the back of yesterday and looking at #DuckDB and the UI extension, I did a quite write-up.
Other than the issue I had around doing a lot of schema changes, it's a really nice UI which doesn't make you feel like you've ended up with the "entry level" interface.
I had a little time for a change and so thought I’d spend a bit of time exploring something different. One of the things that I find weird is the number of devs who say that they “can’t do SQL”. SQL is one of those languages that was created to allow business people to query a database using a language which makes sense to them. It has definitely grown since that lofty ideal, and of course, each database system has its own variation (called a dialect).
Also been playing with #DuckDB UI this afternoon. It’s a great place to start for exploration, and if you don’t have any UI tools available to you.
Did find out that if you’re making a lot of schema changes then it gets itself into a mess and won’t open again. So had to drop and recreate the db file a few times.
Probably do a write up on it tomorrow. Along with a Microsoft Fabric RTI demo for later in the week
Full-Text Search with DuckDB
https://peterdohertys.website/blog-posts/full-text-search-w-duckdb.html
Here's a blog post about my initial experiments with full-text search in #DuckDB.
TL;DR It's a good, simple FTS solution for unconventional data sets. If you do need a more fully-fledged FTS solution (e.g. #postgres then there's a clear migration path.
https://peterdohertys.website/blog-posts/full-text-search-w-duckdb.html
I wrote a blog post about my initial rounds of experimentation with #DuckDB.
TL;DR I came away very impressed and am excited about using it in the future.
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