Best used with an AI agent

40+ live apps, open data APIs, MCP servers, and 200+ guides - more than anyone wants to click through. Point your AI here and it reads the whole map and does the work: finds the tool, pulls the data, runs the analysis, and hands you the links.

Here for the open-source code? Your agent finds the right repo for you - and can even clone and deploy it.

Prefer to explore on your own? Go right ahead.

Paste this to Claude Code, Codex, or any AI agent:
Go to tigzig.com and read tigzig.com/llms.txt. It is a practitioner toolkit - 40+ analytics apps, open no-auth data APIs, MCP servers, open-source repos (github.com/amararun), and 200+ build guides. Help me [your task]. Surface the exact links; where there is an API or MCP, call it directly; and if I want to self-host, find the repo and help me deploy it.

VOICE MODE - Querying & Analyzing Data with Custom GPT AWS - Azure Data Warehouse

Published: July 27, 2024

See me talking to Custom GPT in voice mode. At the backend, it is talking to Azure Data Warehouse (MySQL). ... and analyzing profile of campaign responders ..

... am inserting conditional fields, asking questions about data, creating distributions based on calculated fields, and generating charts.

Not in the video, but I have also carried out a bunch of other tasks in voice mode: creating summary tables, merging summary tables back with modeling datasets, appending tables, dropping tables, etc.

Applications

Senior Leadership Voice Dashboards / Adhoc query support / Quick Queries & Charts / Rapid data transformations / Intelligent IVR / Employee queries ... numerous applications ..

This is Part-2 of my series on connecting to and analyzing data on live data warehouses on AWS & Azure via Custom GPT and LLM Apps.

Link to Part-1

[DW Series -Part 01] Analyze Live Data | AWS–Azure DW | via Custom GPT & LLM Apps

Part-01: Lighthearted Introduction.

UPCOMING EPISODES : COMING NEXT

GPT-LLM Capability Demonstration Videos

Data transformations, analysis, charts, table operations, inter-warehouse operations, operating on large data sets, ML model build.....and limitations, caveats & constraints

How-To Guides

With Codes / Schemas / GitHub Repos

Connecting to data warehouses, deploying FastAPI Server, GPT action schemas, deploying on external LLM Apps, security issues, LLM cost & options, prototype warehouse setup on AWS & Azure.

👉 With special focus on how to use GPTs to get all this done quickly and efficiently