---
title: "GenAI App | LLM Analytics Assistant: Simplifying Data Transformation & Insights. AWS & Azure MySQL DW Example"
slug: genai-llm-app-analytics-assistant-aws-azure-mysql
date_published: 2024-07-27T12:28:22.798Z
original_url: https://www.tigzig.com/post/genai-llm-app-analytics-assistant-aws-azure-mysql
source: migrated
processed_at: 2025-12-02T10:00:00.000Z
---

# GenAI App | LLM Analytics Assistant: Simplifying Data Transformation & Insights. AWS & Azure MySQL DW Example

**NEW**

My open-source platform with a ton of micro-apps and tooling's for AI driven analytics

Text to SQL / connect to ANY data-warehouse on the fly/ direct file upload to data-warehouse table / create temporary database on the fly / python charts / statistical analysis

Realtime-voice connected to database - OpenAI new WebRTC API & Eleven Labs

And more ....

3rd part of the series on LLM Analytics Assistant Apps

Demonstrating data transformation and analysis on AWS MySQL via an LLM App. The app is deployed on my public website (outside of GPT Store, access-controlled section).

I cover 3 areas:

> **LLM APP DEMO**

**Data Wrangling & Analysis:** prototype customer table and transaction table with a million to 10 million records, creating summaries and merging data into new tables with additional variables... analyzing and creating customer profiles. All instructions in natural language... sometimes fuzzy and unclear... and sometimes with spellos...

> **BASIC ARCHITECTURE**

Similar to one that I am currently using on a live client project.

**LLM App Build and UI:** using Flowise AI. Open-source. Allows for rapid deployment. Powerful capabilities. Many other options - e.g. custom build with React/Next.js that can link up to company SSO and authentications.

**Model Choice:** trade-offs between pricing, speed, response quality, and security/privacy. Premium model vs. open-source on-prem solution.

**Architecture Flexibility:** FastAPI processing server. Separate from the main system, making it reusable with different UI apps and backend databases.

> **COST CONSIDERATIONS**

**Cost Example:** ran 478 API requests/queries over 10 hours with GPT-3.5, costing around $1... working with the 1 million-10 million dataset referred to above... also discuss optimization strategies...

**Choosing LLM models:** depends on use case. e.g. Multi-LLM option...for difficult tasks, use an expensive model, and for simpler tasks, use a lower cost model.... or On-Prem solution for specific use cases.

**Full Data Ingestion** by the LLM model is not always necessary... can significantly increase costs... potentially increasing by 100 times or more. For many use cases, processing can be done separately, and the LLM only passes SQL queries/Python commands.

**Split Workflow Approach:** for scenarios requiring full data ingestion, split the workflow into multiple modules. LLM to only ingest the necessary and smallest amount of data directly... process the rest of the data separately.

> **UPCOMING VIDEOS AND POSTS**

Currently preparing detailed tutorials and step-by-step guides covering code, tips, and leveraging GPTs to develop apps. In future videos and posts, I will also cover areas like : processing with on-prem solutions, multiple LLM approaches, segregation of Python processing vs. MySQL processing, machine learning model builds, selective accesses, and more.