Feature/DisposablePixels

CNAME

@@ -1 +1 @@
-
+www.cubometrics.ca

_config.yml

@@ -3,13 +3,14 @@
 #
 
 # Name of your site (displayed in the header)
-name: Your Name
+name: CuboMetrics
 
 # Short bio or description (displayed in the header)
-description: Web Developer from Somewhere
+description: Build Smarter, Query Better
 
 # URL of your avatar or profile pic (you could use your GitHub profile pic)
-avatar: https://raw.githubusercontent.com/barryclark/jekyll-now/master/images/jekyll-logo.png
+avatar: /images/cubometrics.png
+#https://raw.githubusercontent.com/barryclark/jekyll-now/master/images/jekyll-logo.png
 
 #
 # Flags below are optional
@@ -21,12 +22,12 @@ footer-links:
   email:
   facebook:
   flickr:
-  github: barryclark/jekyll-now
+  github: jorgerocha10
   instagram:
-  linkedin:
+  linkedin: jorge-rocha-b863b831
   pinterest:
   rss: # just type anything here for a working RSS icon
-  twitter: jekyllrb
+  twitter: 
   stackoverflow: # your stackoverflow profile, e.g. "users/50476/bart-kiers"
   youtube: # channel/<your_long_string> or user/<user-name>
   googleplus: # anything in your profile username that comes after plus.google.com/
@@ -37,11 +38,11 @@ footer-links:
 disqus:
 
 # Enter your Google Analytics web tracking code (e.g. UA-2110908-2) to activate tracking
-google_analytics:
+google_analytics: G-C89XSHP9RZ
 
 # Your website URL (e.g. http://barryclark.github.io or http://www.barryclark.co)
 # Used for Sitemap.xml and your RSS feed
-url:
+url: cubometrics.ca
 
 # If you're hosting your site at a Project repository on GitHub pages
 # (http://yourusername.github.io/repository-name)

_posts/2025-7-24-What-is-text-to-SQL.md

@@ -0,0 +1,131 @@
+---
+layout: post
+title: What is Text-to-SQL?
+---
+
+I recently stumbled across a tool called [WrenAI](https://getwren.ai/) that claims you can query any database using plain English. I was a little skeptical at first, but after burning through my free credits, I realized they weren’t bluffing. It felt kind of magical to ask questions in natural language and watch it generate relevant SQL and charts in real time.
+
+As someone who works in data modeling and business intelligence, I know how critical a solid underlying model is. Your semantic and reporting layers are only as good as the foundation they sit on. So when a generative AI tool can consistently return useful SQL, readable tables, and insightful charts, you start to wonder if dashboards as we know them are about to be redefined.
+
+What really caught my attention, though, was how tools like this could help democratize access to data. You don’t need to know SQL or even how to use a BI tool. This abstraction layer means non-technical users can get answers on their own, often in seconds. For data teams, that means fewer repetitive ad-hoc requests like “Can you pull sales by region?” or “Oh wait, can you add distribution channel to that?” and more time spent on better modeling and optimization work.
+
+I was curious enough to dig into [WrenAI GitHub repo](https://github.com/Canner/WrenAI/tree/main) to see how it all worked behind the scenes.
+
+![_config.yml]({{ site.baseurl }}/images/how_wrenai_works.png)
+
+At first glance, it looks a bit overwhelming. But the point of this post, and the ones to follow, is to unpack that complexity and explore how these tools actually work under the hood.
+
+Let’s start by breaking down the architecture diagram above.
+
+![_config.yml]({{ site.baseurl }}/images/diagram_breakdown.png)
+
+---
+
+## 1. Connect (Getting the Data In)
+
+### What it does:
+
+This is where a Text-To-SQL system (e.g. WrenAI) connects to your actual data - databases, data warehouses, and more.
+
+- **Connect**: Add a data source (like Synapse Analytics, Snowflake, Postgres, etc.)
+- **Reads Metadata**: Grabs table names, column types, and other schema details
+
+Think of it like introducing the Text-To-SQL system to your data for the first time.
+
+---
+
+## 2. Modeling (Defining the Semantics)
+
+### What it does:
+
+Before WrenAI can do anything useful, you help it understand your data by defining semantics - descriptions, meanings, and relationships.
+
+- You describe what fields mean (e.g., `order_total` = total sales).
+- It creates a **semantic model** to guide its understanding.
+
+This is where you teach the system your organization’s vocabulary.
+
+---
+
+## 3. Indexing (Organizing the Knowledge)
+
+### What it does:
+
+WrenAI processes your schema, metadata, and semantics, and stores them in a **vector database** for fast and relevant retrieval.
+
+- **Index Processing**: Converts the model into vectorized data
+- **Vector Database**: Makes that knowledge easily searchable
+
+It’s like building a memory of your data that the AI can quickly reference.
+
+---
+
+## 4. Retrieval (Finding the Right Context)
+
+### What it does:
+
+When a user asks a question, WrenAI looks through its vector memory to find the most relevant context - like tables, fields, and relationships.
+
+- **Retrieval Service**: Matches the question to the right metadata
+
+It’s like checking your notes before answering an exam question.
+
+---
+
+## 5. Prompting & Generation (Creating the SQL)
+
+### What it does:
+
+Now WrenAI combines the user’s question with relevant context and forms a prompt for an LLM (like GPT-4, Claude, Gemini, you name it, the frontier models will always return better results) to generate SQL.
+
+- **Prompt**: Merges question + metadata
+- **LLM**: Generates the SQL query
+- **Output Processing**: Validates the query to make sure it works
+
+This is the smart part — translating natural language into working SQL.
+
+---
+
+## 6. Wren UI (The User Interface)
+
+### What it does:
+
+This is what users see. You type your question into a chat-like interface, and WrenAI handles the rest.
+
+- Sends your question to the backend
+- Returns SQL results and visualizations
+
+It’s the conversation window where data questions become answers.
+
+---
+
+## 7. Wren Engine (Query Execution Layer)
+
+### What it does:
+
+Once the SQL is ready, it has to run somewhere. That’s the job of the Wren Engine.
+
+- **Core**: Executes the SQL against the connected data source
+- **Metastore**: Stores schema and semantic models for reuse
+
+This is the engine room — quietly powering the results behind the scenes.
+
+---
+
+## Summary in Plain English
+
+> You connect your data → define what it means → WrenAI processes and remembers it → when someone asks a question, it finds the right context → sends it to an AI to generate SQL → runs the SQL → and gives the user an answer.
+
+---
+
+## Wrapping Up
+
+Exploring WrenAI gave me a glimpse into what the future of analytics could look like: a world where anyone in a company can ask a question and get a useful answer instantly, without waiting days for a report or dashboard. Under the hood, it's a blend of old-school data modeling, modern indexing, and generative AI — all working together.
+
+What surprised me most is how much of this relies on concepts we already understand as data professionals: schemas, semantics, metadata, and SQL. The magic is in how they're combined.
+
+This is just the beginning. In upcoming posts, I’ll explore each piece in more depth, from building semantic models to how prompts are structured and validated, and share what I learn along the way. My goal is to better understand how to make data truly AI-ready, and I hope you’ll follow along. If you’re curious about this space or already exploring it yourself, let’s learn together.
+
+Cheers,
+
+Jorge Rocha