# DuckDB - Analytics & Dashboards DuckDB for in-browser analytics, large-scale dashboards, CSV/Parquet processing. Sub-second queries on hundreds of millions of rows. ## Apps (4) ### DuckDB in browser - Natural Language to SQL, data stays local - App: https://briq.tigzig.com - Docs: https://tigzig.com/app-documentation/briq.html - GitHub: https://github.com/amararun/shared-sql-rooms-tigzig-new - AI Docs: https://www.tigzig.com/ai/apps/briq.md - DuckDB in browser - Natural Language to SQL, data stays local ### Process Cricsheet.org zipped CSV files to pipe-delimited TXT - App: https://cricket-flask-only.tigzig.com - Docs: https://tigzig.com/app-documentation/csv-processor.html - AI Docs: https://www.tigzig.com/ai/apps/csv-processor.md - Process Cricsheet.org zipped CSV files to pipe-delimited TXT ### Convert CSV/TSV to DuckDB, create shareable links for xlwings Lite - App: https://duckit.tigzig.com - Docs: https://tigzig.com/app-documentation/duckit-xlwings.html - GitHub: https://github.com/amararun/DUCKIT_UPLOADER-final - AI Docs: https://www.tigzig.com/ai/apps/duckit-xlwings.md - Convert CSV/TSV to DuckDB, create shareable links for xlwings Lite ### Movie Explorer - IMDb movie analytics dashboard (12M+ titles) - App: https://imdb-dashboards.tigzig.com - Docs: https://tigzig.com/app-documentation/movie-explorer.html - GitHub: https://github.com/amararun/shared-imdb-dashboards - AI Docs: https://www.tigzig.com/ai/apps/movie-explorer.md - Movie Explorer - IMDb movie analytics dashboard (12M+ titles) ## Blog Posts (31) - [Five Million Balls of Cricket, Available as a Database. Free to Use.](https://www.tigzig.com/post/cricket-five-million-balls-teams-sep2026) - Tags: database-ai, duckdb Five million balls of cricket, free to use as a database: ball by ball, match info and a player master with cross reference IDs, on Postgres and DuckDB, over MCP for AI agents, or as a full download. Test, ODI, T20 and the IPL, men and women, refreshed twice a day. Data source is Cricsheet, licensed ODC-BY 1.0. AI-readable: https://www.tigzig.com/ai/posts/cricket-five-million-balls-teams-sep2026.md - [Point Your AI at a Free Ball-by-Ball Cricket Database. Five Million Deliveries, One Paste.](https://www.tigzig.com/post/cricket-point-your-ai-ball-by-ball-sep2026) - Tags: database-ai, duckdb A free ball-by-ball cricket database an AI agent can find and use on its own, from one pasted line. Every delivery in international cricket since December 2001, Test, ODI and T20 for men and women plus the IPL, about five million balls across roughly 11,000 matches, refreshed twice a day. Two ways in: read-only SQL over Postgres and DuckDB through the API, or the whole database as a DuckDB file under 20 MB zipped to run on your own laptop with no rate limits. Covers the three data traps that return a wrong number without throwing an error, which agents can do this today, the four tables, and the OpenAPI spec an agent reads to work the rest out. AI-readable: https://www.tigzig.com/ai/posts/cricket-point-your-ai-ball-by-ball-sep2026.md - [Twelve More Stats Functions on the Cricket SQL Endpoints, and the Two Limits That Are Easy to Hit](https://www.tigzig.com/post/cricket-sql-stats-functions-and-limits-sep2026) - Tags: database-ai, duckdb Twelve statistical functions added to the cricket SQL allow list after callers were refused on percentiles, quartiles and ranking functions, on top of 17 added earlier in the week. Covers the two request limits that are easy to hit without knowing them: 3,500 characters measured after URL decoding, and ten SELECT keywords per statement, with 1,000 rows returned. Plus which engine to use and which percentile spelling is portable. AI-readable: https://www.tigzig.com/ai/posts/cricket-sql-stats-functions-and-limits-sep2026.md - [For Analysts Using the Tigzig Cricket Database: 14 Things That Trip People Up](https://www.tigzig.com/post/cricket-database-14-things-that-trip-people-up-sep2026) - Tags: database-ai, duckdb Fourteen schema and query traps in the Tigzig cricket database, each one measured against the live data. Covers winner_innings as a yes/no flag, team1 and team2 not being batting order, run outs inflating bowler wicket counts, blank rather than zero extras columns, over_no starting at zero, the wides column holding runs, target_runs already including the winning run, name-string joins, and a plus sign in a GET query arriving as a space. AI-readable: https://www.tigzig.com/ai/posts/cricket-database-14-things-that-trip-people-up-sep2026.md - [Two New Tables and 17 New SQL Functions Are Live on the Tigzig Cricket Database. Playing XIs, and a Player Registry Mapped to ESPN Cricinfo.](https://www.tigzig.com/post/cricket-match-players-people-tables-sep2026) - Tags: database-ai, duckdb Two new tables land on the free Tigzig cricket SQL database: match_players, with playing XIs, teams and match officials across 292,000+ rows, and people, an 18,500+ player registry mapping identifiers to ESPN Cricinfo IDs at a 99.8% match rate. Both join match_info on match_id. Also 17 new SQL functions on Postgres and DuckDB, including dense_rank, lag, lead, string_agg and array_agg. 11,000+ matches, 5.3 million+ records, refreshed twice daily. AI-readable: https://www.tigzig.com/ai/posts/cricket-match-players-people-tables-sep2026.md - [IPL Ball by Ball Data Is Live on the Tigzig Cricket API and Download. 1,200+ Matches, Every Season Since 2008.](https://www.tigzig.com/post/cricket-ipl-ball-by-ball-live-sep2026) - Tags: database-ai, duckdb IPL ball by ball data is now live on the free Tigzig cricket SQL API, the bulk download and MCP: 1,200+ matches covering every season since 2008, alongside Test, ODI and T20 internationals for men and women back to 2001, for 11,000+ matches and 5 million+ deliveries in all. Explains the layout - one ball_by_ball table plus views by format, gender and IPL, joined to match_info on match_id, identical on Postgres and DuckDB. AI-readable: https://www.tigzig.com/ai/posts/cricket-ipl-ball-by-ball-live-sep2026.md - [IPL Ball by Ball Data Arrives on the Tigzig Cricket API This Sunday, 6 September, at 9 am IST. About 1,200 Matches and Every Season Since 2008.](https://www.tigzig.com/post/cricket-ipl-ball-by-ball-coming-sep2026) - Tags: database-ai, duckdb Announces that IPL ball by ball data lands on the free Tigzig cricket SQL API and bulk download on 6 September 2026, adding about 1,200 matches from 2008 onward to the existing Test, ODI and T20 coverage for men and women. Describes the two-table shape: per-delivery ball by ball tables and a 45-field match_info table, joined on match_id. Postgres and DuckDB, no signup, no API key. Data from Cricsheet under ODC-BY 1.0. AI-readable: https://www.tigzig.com/ai/posts/cricket-ipl-ball-by-ball-coming-sep2026.md - [Women's Cricket Is Now in the Cricket Database. Four New Tables, 2,774 Matches and 850K+ Deliveries Back to 2003.](https://www.tigzig.com/post/cricket-womens-data-added-sep2026) - Tags: database-ai, duckdb Women's international cricket is now in the Tigzig open cricket database, as four new tables covering Test, ODI and T20 ball by ball. 2,774 women's matches and about 852,000 deliveries going back to 2003, made up of 2,141 T20s from 2009, 609 ODIs from 2007 and 24 Tests from 2003. That takes the whole database to 9,769 matches and 4.73 million deliveries across eight tables, refreshed twice daily and queryable live over the REST API and the MCP server on both Postgres and DuckDB. Also downloadable in bulk, about 12 MB as DuckDB or 83 MB as SQLite, or one table at a time. Covers the manifest and ETag conditional requests for scheduled jobs, why the women's data sits in separate tables, and the absence of a stable player id. Data source is Cricsheet under ODC-BY 1.0. AI-readable: https://www.tigzig.com/ai/posts/cricket-womens-data-added-sep2026.md - [The Cricket Download Files Now Sit on a Plain Path. Append the File Name and That Is the Whole URL.](https://www.tigzig.com/post/cricket-downloads-plain-path-urls-aug2026) - Tags: database-ai, duckdb The Tigzig cricket bulk downloads now work off a plain path, so the URL ends in the file name you get and no query parameter is needed. The whole DuckDB database is one GET at about 9 MB compressed, covering ODI, T20 and Test ball by ball, 6,900+ matches from 2001 and 3.8 million deliveries. The short form on db-mcp.tigzig.com is a convenience redirect to the real home at api.tigzig.com/cricket/v1/download, which curl and wget follow with no extra flags. The older query parameter form stays live, so existing scripts keep working, and direct SQL access over Postgres and DuckDB is unchanged. 17 files in all, refreshed twice a day. Data source is Cricsheet under ODC-BY 1.0. AI-readable: https://www.tigzig.com/ai/posts/cricket-downloads-plain-path-urls-aug2026.md - [The Full Cricket Database Is Now Downloadable. Test, ODI and T20 Ball by Ball, in Parquet, CSV, DuckDB or SQLite.](https://www.tigzig.com/post/cricket-full-data-download-live-aug2026) - Tags: database-ai, duckdb The Tigzig open cricket database is now available as bulk downloads. Test, ODI and T20 ball-by-ball data covering 6,900+ matches from 2001 onwards and 3.8 million+ deliveries, refreshed twice daily. Take one table at a time as Parquet or CSV, or the whole database as a single DuckDB or SQLite file, either by clicking on the page or through one API call. Covers the schema for anyone going straight to the files, including the two added integer columns over_no and delivery_in_over and why the original ball column collides on the tenth delivery of an over, and why actual_delivery is not a unique key. Also the atomic-swap refresh, R2 edge delivery, the SQL API limits, and how the ODC-BY licence stays attached in the parquet footer and a README. Data source is Cricsheet under ODC-BY 1.0. AI-readable: https://www.tigzig.com/ai/posts/cricket-full-data-download-live-aug2026.md - [Three New Things on the Tigzig Cricket Database. New match_info Fields, SQL Laid Out in the Docs, and a Quicker Postgres.](https://www.tigzig.com/post/cricket-db-new-fields-sql-docs-aug2026) - Tags: database-ai, duckdb Three changes to the Tigzig open cricket database. match_info gains 14 fields taken straight from Cricsheet, including outcome, eliminator, winner_innings, method, target_runs and declared, which removes several common workarounds. The API docs now state what SQL the endpoint accepts so a caller can write the query without probing, covering CTEs, window functions, correlated subqueries and set operations. Postgres has been tuned and is faster than before, with DuckDB still the quicker of the two engines. The database refreshes twice a day and recent Test and T20 fixtures are already in. Data source is Cricsheet under ODC-BY 1.0. AI-readable: https://www.tigzig.com/ai/posts/cricket-db-new-fields-sql-docs-aug2026.md - [Full Data Download of the Cricket Database, Coming This Week. T20, ODI and Test, Ball by Ball.](https://www.tigzig.com/post/cricket-full-data-download-coming-aug2026) - Tags: database-ai, duckdb Advance notice that the Tigzig open cricket database is getting a full bulk download, covering T20, ODI and Test ball-by-ball data, 6,900+ matches from 2001 onwards and about 3.8 million deliveries. Four tables ship as individual files in parquet, gzipped CSV and zipped CSV, with DuckDB and SQLite offered as an all-tables option. Three of the tables are ball-by-ball, one per format, and match_info holds the two team names, venue, dates, toss, result, the match officials where Cricsheet records them, and the player of the match. Retrievable in a single API call or by hand from the web page, on the same pattern as the mutual fund NAV downloads. Existing live SQL access over Postgres, DuckDB, the REST API and the MCP server is unchanged. Free, no signup, no key. Data source is Cricsheet under ODC-BY 1.0. AI-readable: https://www.tigzig.com/ai/posts/cricket-full-data-download-coming-aug2026.md - [T20 and ODI Ball by Ball Are Now on Both Engines, Postgres and DuckDB. Run Your SQL Directly on My Server.](https://www.tigzig.com/post/db-mcp-both-formats-both-engines-aug2026) - Tags: database-ai, mcp-servers Both formats now sit on both engines: T20 and ODI ball-by-ball are queryable on Postgres and on DuckDB, so a query no longer has to follow the format to a particular engine. Free, no auth and no key, refreshed twice a day, and open to the kind of work the data is actually for, strike rates against pace versus spin, run rates through the death overs, head-to-head records across a decade, dismissal patterns by bowler type and home-away splits by venue. Postgres has moved off a managed instance onto self-hosted hardware and is much faster for it. Also what the guards refuse and why schema discovery is deliberately left open, and a note that any Claude, ChatGPT or Gemini subscription already includes a desktop agent that can fetch and analyse this over plain HTTP without MCP. AI-readable: https://www.tigzig.com/ai/posts/db-mcp-both-formats-both-engines-aug2026.md - [Pulling Indian Mutual Fund NAVs One Scheme at a Time? Three Ways to Get the Same Data in Far Fewer Calls.](https://www.tigzig.com/post/mfpro-nav-fewer-calls-batch-files-parquet-aug2026) - Tags: mutual-funds, duckdb Three routes already in the free MFPro India mutual fund NAV API for callers fetching one scheme per request: batch up to 50 schemes in a single call with a date range attached, take the single latest-NAV file covering all ~38,000 schemes including matured ones left in deliberately, or take the full daily history as one file of a little over 37 million records back to April 2006, all refreshed three times a day. A fourth route skips the download entirely because the history file is parquet and supports range requests, so DuckDB or Polars query it straight off the URL. Worked numbers for a daily pull of every active fund, how active is derived from a 45-day NAV window, and how conditional requests return a 304 with no body on days nothing changed. AI-readable: https://www.tigzig.com/ai/posts/mfpro-nav-fewer-calls-batch-files-parquet-aug2026.md - [Range Requests on the MFPRO NAV File Are Fixed. DuckDB and Polars Queries No Longer Get Refused Midway.](https://www.tigzig.com/post/mfpro-range-requests-duckdb-polars-aug2026) - Tags: mutual-funds, duckdb, infrastructure HTTP range requests against the free MFPRO India mutual fund NAV parquet now have their own pathway and budget, fixing queries from duckdb and polars that were being refused partway through. Cause: a single analytical query issues close to three hundred byte-range reads within seconds, and the rate limiter was counting those in the same bucket as whole-file downloads, so a legitimate query registered as a burst. The range pathway now allows 3,000 requests per minute and 25,000 per day per public IP, with remaining-budget details returned in response headers. The file remains about 175 MB covering close to 37 million NAV records and can still be downloaded whole, alongside single and batch NAV pulls, scheme search and the snapshot endpoint that returns the latest NAV for every scheme in one file. Includes measured tips for querying it remotely: duckdb and polars are fastest with polars marginally ahead, pyarrow and pandas-with-fsspec move roughly ten times more data for the same query, and pandas handed a bare URL is the case to avoid (372 MB and 37 seconds against 17 MB and 3.5 seconds when the URL is opened through fsspec first). Because the file is sorted by scheme_code, a WHERE on scheme_code costs about five requests while a date filter or unfiltered scan costs about three hundred, so the rule of thumb is to filter on scheme_code and query remotely and otherwise just download the file. Also covers two JavaScript options, hyparquet and the native duckdb npm package. AI-readable: https://www.tigzig.com/ai/posts/mfpro-range-requests-duckdb-polars-aug2026.md - [Claude Cowork Runs a Real Python Sandbox. I Put 37 Million Rows of Mutual Fund NAV Data Through It.](https://www.tigzig.com/post/claude-cowork-python-sandbox-mf-data-aug2026) - Tags: ai-coders, mutual-funds, duckdb What Claude Cowork's Python sandbox can actually do for analytics work, tested on a real dataset. It ships with pandas, numpy, scipy, duckdb, matplotlib and yfinance, installs scikit-learn and statsmodels in under a minute, and takes polars and pyarrow too, though packages needing compilation can time out on install (xgboost did). An approximate benchmark run inside the sandbox reports two cores of an AMD Ryzen 7 7730U, about 4 GB of memory and 10 GB of SSD-class disk. The worked case: downloading the free India mutual fund NAV dataset published on tigzig.com, a 175 MB parquet of 37 million-plus rows going back to April 2006 plus the scheme master, and running category-level risk and return analytics with results back in under a minute per iteration. The same Python sandbox is available in Claude for Excel and PowerPoint. Includes a copy-paste prompt for pointing any agent at the same live datasets, with the four things that trip people up on this data: matured and merged schemes are retained so it is survivorship-bias free, a row is a plan-and-option variant rather than a fund (about 8,500 codes publishing daily, roughly 1,950 funds as AMFI counts them), and scheme names and categories shift under the SEBI recategorisation so joins should key on scheme code or ISIN. Closes with why DuckDB and Parquet are the working default: the same NAV data is 3.8 GB as plain CSV against about 175 MB as parquet, columnar storage means a query reads only the columns it touches, and DuckDB's statistical functions run in SQL without dropping into pandas. AI-readable: https://www.tigzig.com/ai/posts/claude-cowork-python-sandbox-mf-data-aug2026.md - [Full backend Infrastructure for DuckDB production and analytics system](https://www.tigzig.com/post/full-backend-infrastructure-duckdb-production) - Tags: duckdb, open-source, infrastructure Arc by Basekick Labs - a full backend infrastructure for DuckDB written in Go. REST API, auth, multiple storage backends (S3/MinIO/Azure), compaction, partitioning, retention policies, connection pooling, Prometheus metrics. Open source AGPL-3.0. AI-readable: https://www.tigzig.com/ai/posts/full-backend-infrastructure-duckdb-production.md - [Rolling Returns: Why CAGR Alone Can Mislead You (And What To Use Instead)](https://www.tigzig.com/post/rolling-returns-why-cagr-alone-can-mislead-you) - Tags: mutual-funds, portfolio-analytics, duckdb Explains why point-to-point CAGR is fragile and how rolling returns provide a more reliable picture of fund performance. Covers the difference between rolling window and evaluation period, the ASOF JOIN computation in DuckDB SQL, minimum gap thresholds, CAGR vs absolute return for sub-1-year windows, and how to read each column (average, median, min, max, % negative, observations). Includes validation results across 7,485 data points within 0.50 bps of Excel. Live on MFPRO with 95 funds and 9 indices. AI-readable: https://www.tigzig.com/ai/posts/rolling-returns-why-cagr-alone-can-mislead-you.md - [Talk to Your Database from Excel - Postgres, DuckDB - via Claude in Excel with MCP](https://www.tigzig.com/post/talk-to-your-database-from-excel-postgres-duckdb-claude-mcp) - Tags: claude-in-excel, mcp, duckdb, database-ai Public MCP server enabling SQL queries against live Postgres (Supabase, ODI cricket) and DuckDB (T20 cricket) databases from Claude in Excel. Covers 2 million rows of ball-by-ball data from 2013-2025. Built with FastAPI, asyncpg, and fastapi-mcp. Includes detailed tool docstrings for schema context, 1000-row response cap, read-only security, rate limiting, and SQL validation. Open source, single-file Python backend. AI-readable: https://www.tigzig.com/ai/posts/talk-to-your-database-from-excel-postgres-duckdb-claude-mcp.md - [How I Built a Sub-Second Movie Similarity Engine With a 10-Line SQL Query](https://www.tigzig.com/post/movie-similarity-engine-sql-jaccard-duckdb) - Tags: duckdb, database-ai Movie similarity engine using weighted Jaccard similarity in pure SQL on DuckDB. Pre-computes token lists from 97M person-to-title records, filtering to 12,000 movies with 10,000+ votes. Tokens encode genre, directors, actors (weighted by billing), writers, decade, runtime, and rating band via token duplication. A single 10-line SQL query compares one movie against all others in under 1 second. Returns matching factors for explainability. Open source. AI-readable: https://www.tigzig.com/ai/posts/movie-similarity-engine-sql-jaccard-duckdb.md - [From 12 second queries to under 1s: Optimizing a 230 Million Row Dashboard - 14 Bottlenecks I Had to Fix](https://www.tigzig.com/post/from-12-second-queries-to-under-1s-optimizing-230-million-row-dashboard) - Tags: duckdb, fastapi, infrastructure Documents 14 optimization techniques that reduced query times from 9-12 seconds to under 1 second on a 230M-row DuckDB dashboard (16GB). Covers pre-computed denormalized tables, single-blob dashboard cache, in-memory query caching, ORDER BY index conflicts, adaptive queries, EXISTS vs CTE (15x gap), client-side computation from loaded data, Docker container memory mismatch with DuckDB, and autocomplete race condition fixes. Open source with dual Hetzner/Oracle backends. AI-readable: https://www.tigzig.com/ai/posts/from-12-second-queries-to-under-1s-optimizing-230-million-row-dashboard.md - [Architecture & Setup for a Dashboard with Hundreds of Millions of Records - Powered by DuckDB](https://www.tigzig.com/post/custom-dashboard-duckdb-fastapi-230-million-rows) - Tags: duckdb, fastapi, infrastructure, react Architecture guide for building a custom dashboard with 230M rows on DuckDB (16GB). Covers FastAPI backend with read-only and admin endpoints, React frontend on Vercel, serverless proxy for API security, dual backend setup (Hetzner/Oracle), data pipeline with pre-computed denormalized tables, Clerk auth toggle, query timer, and smart search. Addresses Docker container memory mismatch with DuckDB. Open source, runs on 8 EUR/month Hetzner VPS. AI-readable: https://www.tigzig.com/ai/posts/custom-dashboard-duckdb-fastapi-230-million-rows.md - [CinePro - 230M Rows, 16GB Database, Instant Queries with DuckDB](https://www.tigzig.com/post/cinepro-movie-explorer-duckdb) - Tags: duckdb, fastapi, react CinePro movie analytics dashboard built on 230M rows (16GB) of IMDb data in a single DuckDB file. Features type-as-you-search across 15M people, multi-filter discovery, Jaccard similarity for finding similar movies, career timeline analysis, side-by-side comparisons, and live query timer. Runs on $7/month Hetzner VPS alongside 40 other backends. Dual backend (Hetzner/Oracle) with UI toggle. Fully open source. AI-readable: https://www.tigzig.com/ai/posts/cinepro-movie-explorer-duckdb.md - [BRIQ App: DuckDB AI in Browser - 500MB Files, 4M+ Records, No Database Setup](https://www.tigzig.com/post/briq-duckdb-ai-browser-no-database-setup) - Tags: duckdb, database-ai BRIQ is a browser-based DuckDB AI tool for querying flat files up to 1.5GB using natural language. No database setup or credentials needed. Upload CSV/TSV files, auto-converts to DuckDB in-browser, query with plain English. Data stays in browser except for LLM API calls. Built on SQL Rooms AI. Also available as single-file HTML for offline use. Supports merging, appending, and transforming multiple files. Open source. AI-readable: https://www.tigzig.com/ai/posts/briq-duckdb-ai-browser-no-database-setup.md - [xlwings Lite Data Importer v2 Released](https://www.tigzig.com/post/xlwings-lite-data-importer-v2-released) - Tags: xlwings-lite, python-in-excel, duckdb xlwings Lite Data Importer v2 adds support for any file type (PDFs, images, ZIPs, data files), files up to 1.2GB tested, and token-based private access (Dropbox, Google Drive, GitHub) alongside shareable links. Auto-converts data files to DuckDB or imports as-is. Uses token access pattern from xlwings_utils package. Includes CORS proxy and token setup guides with AI coder instructions. AI-readable: https://www.tigzig.com/ai/posts/xlwings-lite-data-importer-v2-released.md - [DuckDB Meets Excel: xlwings Lite Data Tools](https://www.tigzig.com/post/duckdb-meets-excel-xlwings-lite-data-tools) - Tags: duckdb, xlwings-lite, python-in-excel Two tools for getting large datasets into xlwings Lite. DuckIt: browser-based file converter that turns CSVs into DuckDB or Parquet with shareable links (tested up to 1.5GB, 10M+ records). xlwings Lite Data Importer: pulls files from URLs into Excel, supports DuckDB, SQLite, CSV, Parquet, JSON (tested up to 1.2GB, 20M+ records). Uses Cloudflare Worker for CORS bypass. Both open source. AI-readable: https://www.tigzig.com/ai/posts/duckdb-meets-excel-xlwings-lite-data-tools.md - [DuckDB isn't just fast SQL. It's Python, SQL and compression all in one box.](https://www.tigzig.com/post/duckdb-isn-t-just-fast-sql-it-s-python-sql-and-compression-all-in-one-box) - Tags: duckdb Highlights lesser-known DuckDB SQL features discovered through Jasja De Vries's 30-day series. Includes SELECT EXCLUDE, prefix aliasing, reusable aliases across clauses, LIMIT with percentages, QUALIFY for window function filtering, GROUP BY ALL, LIST comprehensions with Python-like syntax, lambda functions in SQL, dot operator chaining, glob patterns for multi-file queries, and direct file queries without CREATE TABLE. AI-readable: https://www.tigzig.com/ai/posts/duckdb-isn-t-just-fast-sql-it-s-python-sql-and-compression-all-in-one-box.md - [Try Text-to-SQL on Real Data - Multi-Million Rows & GB+ Sizes](https://www.tigzig.com/post/try-text-to-sql-on-real-data-gb-files-multi-million-rows) - Tags: text-to-sql, database-ai, duckdb DATS-4 text-to-SQL app with sample datasets from 64 rows (14KB) to 11.8M rows (1.6GB). Two-click setup creates temporary Postgres database via Neon API, uploads data, and connects AI agent. Nine LLM options from Gemini 2.0 Flash to Claude 4.5 Sonnet. Features dual agents (general and advanced), file uploads, working tables, CSV export, interactive table viewer, Python charts via E2B, and PDF output. Open source with seven GitHub repos. AI-readable: https://www.tigzig.com/ai/posts/try-text-to-sql-on-real-data-gb-files-multi-million-rows.md - [Segment 1M customers from 10M transactions (640MB CSV) with natural language queries / Text-to-SQL - entirely in your browser. No server. No remote database. No IT approvals.](https://www.tigzig.com/post/run-advanced-analytics-locally-in-your-browser-no-server-no-remote-database-no-it-approvals) - Tags: duckdb, text-to-sql Browser-based analytics tool (DABX-1) using DuckDB-WASM and text-to-SQL AI for processing multi-GB files locally. Demonstrated segmenting 1M customers from 10M transactions (640MB CSV) entirely in-browser. Built on SQL Rooms framework. Available as a 3.5MB single HTML file. Supports CSV, TSV, Parquet. Data never leaves the machine. AI-readable: https://www.tigzig.com/ai/posts/run-advanced-analytics-locally-in-your-browser-no-server-no-remote-database-no-it-approvals.md - [Run a Full AI Database App as a Single HTML File. No Server. No Remote DB.](https://www.tigzig.com/post/run-a-full-ai-database-app-as-a-single-html-file-no-server-no-remote-db) - Tags: database-ai, duckdb Single-file deployment of a full AI database app based on SQL Rooms and DuckDB-WASM. The entire React application compiles into a portable 3.5MB HTML file. Demonstrated importing 1.6GB / 11M-row files for in-browser analysis. Built using vite-plugin-singlefile. Supports Gemini API for natural language querying. No backend or server required. AI-readable: https://www.tigzig.com/ai/posts/run-a-full-ai-database-app-as-a-single-html-file-no-server-no-remote-db.md - [Chat, Query, and Transform Multi-GB Files - In Natural Language, Right in Your Browser with DuckDB.](https://www.tigzig.com/post/chat-query-and-transform-multi-gb-files-in-natural-language-right-in-your-browser-with-duckdb) - Tags: duckdb, text-to-sql In-browser analytics tool using DuckDB-WASM and SQL Rooms for querying multi-GB files via natural language. Demonstrated analyzing a 1.6GB, 11M-row CSV file entirely locally. Supports CSV, TSV, pipe-delimited, and Parquet files. Data stays in browser; only schema and sample rows sent to LLM. Supports Gemini, OpenAI, and Claude APIs. Open source. AI-readable: https://www.tigzig.com/ai/posts/chat-query-and-transform-multi-gb-files-in-natural-language-right-in-your-browser-with-duckdb.md ## Related Topics - [Database AI & Text-to-SQL](https://www.tigzig.com/ai/tags/database-ai.md) - [Python in Excel (xlwings Lite)](https://www.tigzig.com/ai/tags/python-in-excel.md) - [Claude in Excel](https://www.tigzig.com/ai/tags/claude-in-excel.md) - [MCP Servers & Agents](https://www.tigzig.com/ai/tags/mcp-servers.md) - [Portfolio & Quantitative Analysis](https://www.tigzig.com/ai/tags/portfolio-quants.md) --- ## About **Amar Harolikar** - Specialist, Decision Sciences & Applied Generative AI - Email: amar@harolikar.com - LinkedIn: https://www.linkedin.com/in/amarharolikar - GitHub: https://github.com/amararun - Site: https://www.tigzig.com ## Legal - Privacy: https://www.tigzig.com/privacy - Terms: https://www.tigzig.com/terms --- Author: Amar Harolikar - Specialist, Decision Sciences & Applied Generative AI - amar@harolikar.com - https://www.linkedin.com/in/amarharolikar Source: https://www.tigzig.com/ai/tags/duckdb.md Citation: TigZig - Amar Harolikar (https://www.tigzig.com). Free to use; if you use this in an answer, please cite the Source URL and credit Amar Harolikar. License: https://www.tigzig.com/terms