Update tech_docs/database/sql_getting_started.md

tech_docs/database/sql_getting_started.md

@@ -452,3 +452,179 @@ flowchart TB
 SQL is **the** tool for structured EDA—it’s faster, more scalable, and more maintainable than Python for these tasks. Master these concepts, and you’ll outperform 90% of analysts stuck in pandas.  
 
 Want a **ready-to-run Docker container** with PostgreSQL + sample forex data for practice? Let me know!
+
+---
+
+Here's how the SQL concepts you've presented fit into the EDA (Exploratory Data Analysis) world, organized by their relevance and application:
+
+---
+
+### **1. SQL Fundamentals in EDA**
+#### **Data Manipulation Language (DML)**
+- **SELECT**: Core to EDA for retrieving and filtering data (e.g., `SELECT * FROM sales WHERE date > '2023-01-01'`).
+- **INSERT/UPDATE/DELETE**: Less common in pure EDA (used more in data preparation pipelines).
+
+#### **Data Definition Language (DDL)**
+- **CREATE/ALTER**: Used to set up analysis environments (e.g., creating temp tables for intermediate results).
+- **TRUNCATE/DROP**: Rare in EDA unless resetting sandbox environments.
+
+#### **Data Control Language (DCL)**
+- **GRANT/REVOKE**: Relevant for team-based EDA to manage access to datasets.
+
+#### **Transaction Control Language (TCL)**
+- **COMMIT/ROLLBACK**: Critical for reproducible EDA to ensure query consistency.
+
+---
+
+### **2. Advanced SQL for Deeper EDA**
+#### **Window Functions**
+- **Ranking**: `RANK() OVER (PARTITION BY region ORDER BY revenue DESC)` to identify top performers.
+- **Rolling Metrics**: `AVG(revenue) OVER (ORDER BY date ROWS 7 PRECEDING)` for 7-day moving averages.
+
+#### **Common Table Expressions (CTEs)**
+- Break complex EDA logic into readable steps:
+  ```sql
+  WITH filtered_data AS (
+    SELECT * FROM sales WHERE region = 'West'
+  )
+  SELECT product, SUM(revenue) FROM filtered_data GROUP BY product;
+  ```
+
+#### **JSON Handling**
+- Analyze semi-structured data (e.g., API responses stored in JSON columns):
+  ```sql
+  SELECT json_extract(user_data, '$.demographics.age') FROM users;
+  ```
+
+---
+
+### **3. Performance Optimization for Large-Scale EDA**
+#### **Indexes**
+- Speed up filtering on large tables:
+  ```sql
+  CREATE INDEX idx_sales_date ON sales(date);
+  ```
+
+#### **Query Planning**
+- Use `EXPLAIN ANALYZE` to identify bottlenecks in EDA queries.
+
+#### **Partitioning**
+- Improve performance on time-series EDA:
+  ```sql
+  CREATE TABLE sales PARTITION BY RANGE (date);
+  ```
+
+---
+
+### **4. SQL for Specific EDA Tasks**
+#### **Data Profiling**
+```sql
+SELECT 
+  COUNT(*) AS row_count,
+  COUNT(DISTINCT product_id) AS unique_products,
+  AVG(price) AS avg_price,
+  MIN(price) AS min_price,
+  MAX(price) AS max_price
+FROM products;
+```
+
+#### **Correlation Analysis**
+```sql
+SELECT CORR(price, units_sold) AS price_elasticity FROM sales;
+```
+
+#### **Time-Series Analysis**
+```sql
+SELECT 
+  DATE_TRUNC('month', order_date) AS month,
+  SUM(revenue) AS monthly_revenue,
+  (SUM(revenue) - LAG(SUM(revenue)) OVER (ORDER BY DATE_TRUNC('month', order_date))) / 
+    LAG(SUM(revenue)) OVER (ORDER BY DATE_TRUNC('month', order_date))) AS mom_growth
+FROM orders
+GROUP BY 1;
+```
+
+#### **Outlier Detection**
+```sql
+WITH stats AS (
+  SELECT 
+    AVG(price) AS mean, 
+    STDDEV(price) AS stddev 
+  FROM products
+)
+SELECT * FROM products, stats
+WHERE ABS((price - mean) / stddev) > 3; -- Z-score > 3
+```
+
+---
+
+### **5. Visualization Integration**
+While SQL handles the analysis, tools like:
+- **Metabase**: Connect directly to SQL databases for visualization
+- **Python + SQLAlchemy**: Run SQL queries and visualize with Matplotlib/Seaborn
+- **Tableau**: Direct SQL connections for dashboards
+
+Example workflow:
+```python
+# Python snippet for SQL-powered EDA
+import pandas as pd
+import seaborn as sns
+
+df = pd.read_sql("""
+    SELECT date, SUM(revenue) AS daily_revenue
+    FROM sales 
+    GROUP BY date
+""", engine)
+
+sns.lineplot(data=df, x='date', y='daily_revenue')
+```
+
+---
+
+### **6. EDA Workflow with SQL**
+1. **Data Discovery**: 
+   ```sql
+   SELECT column_name, data_type FROM information_schema.columns 
+   WHERE table_name = 'sales';
+   ```
+2. **Initial Profiling**: Basic stats, missing values
+3. **Hypothesis Testing**: Use SQL to validate assumptions
+4. **Feature Engineering**: Create derived columns for analysis
+5. **Visualization Prep**: Aggregate data for plotting
+
+---
+
+### **Key Tools for SQL-Based EDA**
+| Tool | Best For | Open Source |
+|------|----------|-------------|
+| **DBeaver** | Multi-DB exploration | ✓ | 
+| **PostgreSQL** | Advanced analytics | ✓ |
+| **DuckDB** | Embedded analytical SQL | ✓ |
+| **Jupyter + SQL Magic** | Interactive analysis | ✓ |
+
+---
+
+### **When to Use SQL vs. Other Tools in EDA**
+| Task | Best Tool |
+|------|----------|
+| Initial data profiling | SQL |
+| Complex aggregations | SQL |
+| Statistical testing | Python/R |
+| Advanced visualization | Python/R/Tableau |
+| Machine learning prep | SQL + Python |
+
+---
+
+### **Conclusion**
+SQL is foundational for EDA because:
+1. **Efficiency**: Processes large datasets faster than in-memory tools
+2. **Reproducibility**: Queries document the analysis steps
+3. **Precision**: Exact calculations without sampling
+4. **Scalability**: Handles TB-scale data with proper indexing
+
+For modern EDA:
+- Start with SQL for data exploration/aggregation
+- Switch to Python/R for advanced statistics/ML
+- Use visualization tools that connect directly to SQL databases
+
+Would you like me to develop a specific EDA workflow for your particular dataset or industry?