From ae21e7227fe5c7a8440e13f761b02c09c02fe2f9 Mon Sep 17 00:00:00 2001 From: medusa Date: Wed, 18 Jun 2025 04:37:56 +0000 Subject: [PATCH] Update tech_docs/database/sql_getting_started.md --- tech_docs/database/sql_getting_started.md | 178 +++++++++++++++++++++- 1 file changed, 177 insertions(+), 1 deletion(-) diff --git a/tech_docs/database/sql_getting_started.md b/tech_docs/database/sql_getting_started.md index ece0974..6443a33 100644 --- a/tech_docs/database/sql_getting_started.md +++ b/tech_docs/database/sql_getting_started.md @@ -451,4 +451,180 @@ flowchart TB ### **Final Thought** 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! \ No newline at end of file +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? \ No newline at end of file