DuckDb makes so much of my life easier, though I've never used it for large problems. The ability to run `select * from 'data.json'` is just lovely. The fact that it's also a powerhouse is so impressive, I'd usually expect a project to be good at small problems (like mine) xor large problems, but not both
The data scientists I work with use this. Why do they use it? I don't really know much about it, but I've noticed they use it quite often. I mainly use MySQL or PostgreSQL. What are the advantages of DuckDB? It seems like they usually use it as an alternative to Pandas.
If you're reading this and curious: consider writing a duckdb community extension* or contributing to an existing one*
duckdb is becoming a kind of data superglue between a lot of data ecosystems (GIS, observability, analytics, lakehouses, object storage, etc) that don't talk to each other typically, and it's worth checking out in 2026.
duckdb is so nice coupled with claude code. It extensive file support and some very interesting decisions on local caching data (like from S3 or snowflake) makes it easy to slice and dice almost any kind of tabular data.
DuckDB is amazing for any sort of fast data analysis when the data is small enough that it can fit on your laptop
Recently at work I've been using it to analyse the Claude code sessions of every engineer at our company (that we upload to S3) and it's been extremely helpful to help us find gaps in devex and have clear metrics to back up the impact of fixing them
Another thing it's been really useful for has been getting metrics on Claude skills usage and then dive into use-cases by looking at the transcripts
Other engineers that had never touched DuckDB were so impressed with how easy it is for AI agents to write queries on our dataset
Because I can just write SQL to access all my data. The data comes from CSVs, SQLite, MySQL, Postgres, ...
I can write SQL everywhere. I write SQL from within my R scripts, I write SQL from within my Python scripts. SQL is THE language for querying databases, others came and went, SQL will stay with us for the next decades. Even the dbplyr people say (parapfhrased): If you can't express what you want with the tidyverse, just write your SQL and load the result as a dataframe.
> ..In-process means there's no server. You don't connect to DuckDB; you load it as a library inside your program, the same way you'd load NumPy or Polars
Does it mean it can perform all statistical computations as well if I want to use for algo trading?
If DuckDB is so fast and has no data transfer overheads, does it need all this typical SQL machinery with filtering and joining via SELECT queries? Wouldn't it be simpler and faster to return all data to the caller code (all table rows, but only requested columns) and let it perform all other necessary data processing logic?
I'm just curious - is duckdb too slow for people? This benchmark from clickhouse shows it being fairly slow compared to some options: https://jsonbench.com/
Hi, DuckDB engineer here, to tackle this problem of efficiently storing and querying semi-structured data (aka JSON), we've put in a lot of work into the VARIANT type and still continue to pour a lot more work into it regularly, so expect to see some developments on this area in the upcoming major release!
The one huge caveat for anyone that cannot use dynamic linking e.g. in an AppStore context, DuckDB isn’t a great choice. It’s very hard to statically link extensions.
This is where Arrow wins I think. Arrow CPP for example has very portable builds and the C interface is very usable for building bindings.
DuckDB is excellent, but it’s more a black box than a library.
Edit: after a conversation with a robot, it would seem that the DuckDB and ArrowCPP C APIs are complimentary, so it's very possible to have Arrow CPP and DuckDB to coexist in an app, each with its own strength. Arrow CPP doen't have a simple SQL story for example.
The only reason I know and use DuckDB is because my (internal, private-use-only, experimental) vibe coded projects use it a ton. I didn't pick it - LLMs did. Until this article, I wasn't aware of what it actually is capable of.
Most of these projects use JSON(L) files for storage, and duckdb to process them.
Is everything becoming columnar? Parquet stores data per column instead of per row because it improves compression. I get that. Arrow apparently is columnar, and now DuckDB also gets its efficiency by treating data as columns instead of rows?
I still need to wrap my head around how that works, but it's a fascinating development.
I use duckdb HEAVILY at work and it's been a game changer. I'm sifting through terabytes of data multiple times a day, mixing, matching, updating, filtering, DuckDB is second to none. For anyone that hasn't used it: you are missing out.
I recently got a task to sift through a daily drop of parquet files that need to be processed and given a UI for arbitrary queries by a non-technical team. Naively I started by looking at redshift, Athena/glue, etc. I spun my wheels for a week and barely made progress, resulting in a miserably fragile, slow, overly complicated, and/or expensive set of underwhelming PoCs. So I decided to give DuckDB a shot and I got the whole project done in under a day. It was so intuitive and runs so fast I was sure I was missing something. I wasn’t. What an absolute joy to use, and the supporting documentation is outstanding. My problem now is it’s my favorite hammer and I want everything to be a nail.
I've never been that strong of an engineer. TIL that at one of my internships I was building DuckDB but for the company's private use cases. Well, trying to anyways. I didn't really get the whole picture, the pieces did not fit into place.
Didn't get the return offer obviously, probably because I didn't make the connection (or really a coherent narrative of what I was building). RIP. You live and you learn, I guess.
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[ 3.1 ms ] story [ 68.8 ms ] threadThis was a major factor in my initial adoption. Since then it has stuck because it’s also absurdly capable, versatile, and fast.
If it wasn’t so easy to use I suspect I wouldn’t have adopted it when I did. The ergonomics are crazy. It still impresses me regularly.
duckdb is becoming a kind of data superglue between a lot of data ecosystems (GIS, observability, analytics, lakehouses, object storage, etc) that don't talk to each other typically, and it's worth checking out in 2026.
* https://github.com/duckdb/extension-template * https://duckdb.org/community_extensions/
Recently at work I've been using it to analyse the Claude code sessions of every engineer at our company (that we upload to S3) and it's been extremely helpful to help us find gaps in devex and have clear metrics to back up the impact of fixing them
Another thing it's been really useful for has been getting metrics on Claude skills usage and then dive into use-cases by looking at the transcripts
Other engineers that had never touched DuckDB were so impressed with how easy it is for AI agents to write queries on our dataset
It also works great for data that doesn't fit on my laptop.
Better perf + SQL is that mostly it?
I can write SQL everywhere. I write SQL from within my R scripts, I write SQL from within my Python scripts. SQL is THE language for querying databases, others came and went, SQL will stay with us for the next decades. Even the dbplyr people say (parapfhrased): If you can't express what you want with the tidyverse, just write your SQL and load the result as a dataframe.
> ..In-process means there's no server. You don't connect to DuckDB; you load it as a library inside your program, the same way you'd load NumPy or Polars
Does it mean it can perform all statistical computations as well if I want to use for algo trading?
This is where Arrow wins I think. Arrow CPP for example has very portable builds and the C interface is very usable for building bindings.
DuckDB is excellent, but it’s more a black box than a library.
Edit: after a conversation with a robot, it would seem that the DuckDB and ArrowCPP C APIs are complimentary, so it's very possible to have Arrow CPP and DuckDB to coexist in an app, each with its own strength. Arrow CPP doen't have a simple SQL story for example.
Most of these projects use JSON(L) files for storage, and duckdb to process them.
I still need to wrap my head around how that works, but it's a fascinating development.
Still a bit raw, but getting there
I've never been that strong of an engineer. TIL that at one of my internships I was building DuckDB but for the company's private use cases. Well, trying to anyways. I didn't really get the whole picture, the pieces did not fit into place.
Didn't get the return offer obviously, probably because I didn't make the connection (or really a coherent narrative of what I was building). RIP. You live and you learn, I guess.