DuckDB

DuckDB AI

DuckDB AI

DuckDB AI

DuckDB AI

DuckDB AI

DuckDB AI

DuckDB AI

DuckDB AI

DuckDB AI

DuckDB AI

Tips for BI analysis with Claude? My results so far are shockingly bad compared to general coding

I have a lot of hands-on experience with developing R pipelines to ingest large, live, very dirty datasets and produce relatively straightforward BI-type analyses. Trends, completion rates, revenue etc. I am currently working on a project with a small, live, moderately dirty dataset. The output should be simple analyses eg of lead quality, time to deal, revenue per product line. I am developing this project with Python and DuckDB. I am having incredible difficulty with getting Claude (Code) to coherently do this work, even when taking the pipeline design process step by step. I am always using Opus 4.7 High, and regularly experiencing Claude contradict clear instructions I gave it even within the last 5 minutes. It gives extremely generic names to variables and then very soon will completely misunderstand what the variables mean. It leaps to fixing problems without having any understanding of them and invents generic terminology that disagrees with the established project terms. My hypothesis is that this is an artifact of the data exploration. Inevitably as I explore the dirty data while building this pipeline I'm constantly uncovering new edge cases that need to be accounted for, and I guess this likely pollutes the context very quickly. Likely also Claude is more hesitant to codify "findings" than would be normal in a data pipeline, because it's engineered for more... deterministic (?) programming situations where findings are often meant to be fixed and forgotten. I am planning a few changes to my normal workflow: Much smaller context window, potentially even clearing after every small adjustment to the pipeline Strictly aligning with enterprise-grade standards (eg OpenTelemetry, Databricks Medallions) even for this small project Developing an extremely strict and exhaustively clear variable naming structure so that as Claude writes the tokens for each variable it cannot avoid understanding its meaning (eg medallion___source_module___data_scope___data_qualifiers___stat_type___time_window). Enforce constant linting of 2 and 3 through a hook. Anything else that can be recommended? One thing I'm attempting to do is "go with the flow" and try to figure out what Claude "wants" to do, then strictly codify that... but it seems like most often Claude is just doing random things. Any advice for that? submitted by /u/unwritten734 [link] [comments]

I built an MCP server that lets Claude query 200GB CSV files in under a second

I was trying to use Claude to analyze a large CSV file via MCP. The setup worked but the query speed was killing the experience — waiting 3-4 seconds between each Claude query made the whole workflow feel broken. So I built csvql — a SQL engine that runs as an MCP server. Claude connects to it directly and queries CSV files via natural language. The difference in workflow is significant. Claude fires a query, gets results in milliseconds, fires another. It feels like a real analytical session rather than waiting for a database to wake up. Setup is one line: csvql --mcp Then in Claude Desktop config: { "mcpServers": { "csvql": { "command": "/usr/local/bin/csvql", "args": ["--mcp"] } } } That's it. No database to spin up, no schema to define, no Python environment. Point Claude at any CSV file and start asking questions. What you can ask Claude: "Show me the top 10 customers by revenue this year" "How many orders per month in 2025?" "Which employees have no department assigned?" "Average delivery time by region, only where average exceeds 5 days" "Join orders with customers, filter by UK region" Full SQL support under the hood — JOIN, GROUP BY, HAVING, DISTINCT, CASE WHEN, date functions, everything. Why it's fast: The engine is written in Zig with SIMD parsing and memory-mapped I/O. 1M row queries run in 20ms. Memory usage stays under 2MB regardless of file size. Why I built this: I kept hitting a wall doing interactive data analysis with Claude on large files. Existing tools were either too slow or too heavy to set up. I'd also been wanting to learn Zig properly for a while — the performance constraints of this problem forced me to understand systems programming at a level I never had to before. What started as a weekend experiment to see if I could beat DuckDB turned into a full query engine. If you're one or two steps behind where I was — curious about systems programming or Zig but haven't had a real problem to drive it — this kind of project is a good forcing function. The performance feedback loop is immediate and brutal, which makes learning fast. Happy to answer questions on setup, usage, or how the engine works under the hood. submitted by /u/melihbirim [link] [comments]

Built a zero-infra Claude Code cost monitor using Claude Code

I kept hitting my token limit mid-sprint with no clue which prompts were responsible. So I used Claude Code to build something that shows me in real-time. Claude Code exports OTel telemetry for every prompt, API call, and tool execution but nothing connects them together. I pointed it at LaminarDB, a streaming SQL engine I’ve been working on in Rust, and now it correlates everything as events come in. Turns out one prompt cost me $7 while another did the same thing for $0.26. The 5-hour rolling usage bar means the token limit is finally something you can see coming. The whole setup is one process and a local folder. What you see in the screenshot is a real session. How it works: LaminarDB receives OTel over gRPC, flattens protobuf into Arrow RecordBatches, and runs streaming SQL with temporal joins. Claude Code fires separate events for prompts, API calls, and tool results sharing a prompt.id. The temporal join matches them within a time window so you get one complete picture per prompt. Results push to WebSocket for the live dashboard and sink to local Delta Lake files you can query later with DuckDB. I built most of this with Claude Code itself so I was watching my costs climb while building the thing that tracks them. Weird feedback loop but good for testing. Happy to share the setup if anyone wants to try it.​​​​​​​​​​​​​​​​ submitted by /u/SillyBuffalo1108 [link] [comments]

I built a text-to-SQL MCP for all your databases

Been tinkering with MCP servers for a while and got tired of how much boilerplate it takes to give Claude access to my databases and explain them. So I built Statespace: the whole idea is that you declare your MCP's instructions AND tools in Markdown/YAML. Here's a minimal example for Postgres: README.md --- tools: - [psql, -d, $DB, -c, { regex: "^SELECT\\b.*" }] --- # Instructions - Learn the schema by exploring tables, columns, and relationships - Translate the user's question into a query that answers it That regex field is the permission boundary. Claude can only run queries that start with SELECT. No drops, no updates. That's it. That's your entire MCP app. MCP config: "statespace": { "command": "npx", "args": ["statespace", "mcp", "path/to/README.md"], "env": { "DB": "postgresql://user:pass@host:port/db" } } Then just ask: claude "How many users signed up last week? ... As the app grows you can add more files (e.g., schema docs, Python scripts, whatever) and list more tools in the YAML frontmatter. Multi-page apps are also supported Supports PostgreSQL, MySQL, SQLite, Snowflake, MongoDB, DuckDB, MSSQL, and just about any database with a CLI. Happy to answer questions! GitHub Repo: https://github.com/statespace-tech/ssp A ⭐ on GitHub really helps with visibility! submitted by /u/Durovilla [link] [comments]

Duckdb-skill: DuckDB-powered skills for data exploration and session memory

The skills supported include: + read-file and query - uses DuckDB's CLI to query data locally, unlocking easy access to any file that DuckDB can read. + read-memories a clever idea to store your Claude memories in DuckDB and query them at blazing speed. These are powered by two additional skills: + attach-db - gives Claude a mechanism to manage DuckDB state through a .sql file linked to your project. + duckdb-docs - uses a remote DuckDB full-text search database to query the DuckDB docs and answer all of your (and Claude's own) questions. Link: https://github.com/duckdb/duckdb-skills Besides the above, duckdb is a really helpful tool if you do any kind of data analysis, Claude usually doesnt default to it but it performs a lot better than pandas, which is usually the default. Does anyone else use it with Claude? submitted by /u/quaintquine [link] [comments]