From 6915ab3ffdbe5239954d7f8b9393dd10ff1e56fc Mon Sep 17 00:00:00 2001 From: medusa Date: Sun, 22 Jun 2025 08:11:27 +0000 Subject: [PATCH] Add tech_docs/database/lotto_project_repo.md --- tech_docs/database/lotto_project_repo.md | 223 +++++++++++++++++++++++ 1 file changed, 223 insertions(+) create mode 100644 tech_docs/database/lotto_project_repo.md diff --git a/tech_docs/database/lotto_project_repo.md b/tech_docs/database/lotto_project_repo.md new file mode 100644 index 0000000..dfd6d64 --- /dev/null +++ b/tech_docs/database/lotto_project_repo.md @@ -0,0 +1,223 @@ +I'm glad that resonated with you! Working with **historical data** (like a 3-digit daily lottery draw) is a great way to practice **OLAP concepts**, time-series analysis, and trend forecasting. Here’s how you can approach it, along with learning resources and tools: + +--- + +### **Step 1: Collect Historical Lottery Data** +- **Example Data Structure (3-digit daily draw):** + ```plaintext + DrawDate | Number | SumOfDigits | EvenOddPattern | DayOfWeek + -----------|--------|-------------|----------------|----------- + 2024-01-01 | 537 | 15 | Odd-Odd-Odd | Monday + 2024-01-02 | 214 | 7 | Even-Odd-Even | Tuesday + ... + ``` +- **Where to Get Data:** + - Public lottery archives (e.g., [state lottery websites](https://www.lotteryusa.com/)). + - APIs (if available) or scrape data (with permission). + - Synthetic data generation (Python’s `pandas`/`numpy` for practice). + +--- + +### **Step 2: Choose Your Tool Stack** +#### **Option 1: SQL-Based OLAP (Best for Scalability)** +- **Tools:** PostgreSQL (with TimescaleDB), Snowflake, BigQuery. +- **Schema Design (Star Schema for OLAP):** + ```sql + -- Fact Table + CREATE TABLE fact_draws ( + draw_id INT, + draw_date DATE, + number INT, + sum_of_digits INT, + pattern_id INT -- FK to dim_patterns + ); + + -- Dimension Tables + CREATE TABLE dim_patterns ( + pattern_id INT, + even_odd_pattern VARCHAR(20) -- e.g., "Odd-Even-Even" + ); + ``` +- **Example Query (Trend Analysis):** + ```sql + -- "Most common Even/Odd patterns by month" + SELECT + EXTRACT(MONTH FROM draw_date) AS month, + dim_patterns.even_odd_pattern, + COUNT(*) AS frequency + FROM fact_draws + JOIN dim_patterns ON fact_draws.pattern_id = dim_patterns.pattern_id + GROUP BY month, even_odd_pattern + ORDER BY month, frequency DESC; + ``` + +#### **Option 2: Python + Jupyter Notebooks (Best for Prototyping)** +- **Libraries:** `pandas`, `matplotlib`, `statsmodels`. +- **Example Analysis:** + ```python + import pandas as pd + + # Load historical data + df = pd.read_csv("lotto_draws.csv") + + # Analyze frequency of numbers + df['number'].value_counts().head(10).plot(kind='bar'); + ``` + ![Example output: Bar chart of top 10 most drawn numbers](https://i.imgur.com/XYZ123.png) + +#### **Option 3: BI Tools (Best for Visualization)** +- **Tools:** Power BI, Tableau, Metabase. +- **Example Dashboard:** + - Heatmap of number frequencies by day of week. + - Time-series of sums of digits (to spot outliers). + +--- + +### **Step 3: Learn Key Techniques** +1. **Time-Series Analysis:** + - Rolling averages (e.g., "7-day moving average of sums"). + - Seasonality detection (e.g., "Are weekends luckier?"). + +2. **Probability Basics:** + - Calculate observed vs. expected frequencies (Chi-square tests). + - Identify biases (e.g., is `111` drawn less often than expected?). + +3. **Machine Learning (Optional):** + - Predict next day’s sum range (regression). + - Cluster patterns (unsupervised learning). + +--- + +### **Step 4: Practice Projects** +1. **Basic:** + - "Which number has appeared most in the last 5 years?" + - "Does the sum of digits follow a normal distribution?" + +2. **Advanced:** + - Build a dashboard showing real-time odds based on history. + - Simulate 10,000 draws to test the "gambler’s fallacy." + +--- + +### **Resources to Learn** +- **SQL for OLAP:** + - Book: *"Data Warehouse Toolkit"* by Kimball (star schema design). + - Course: [Google’s Advanced SQL on Coursera](https://www.coursera.org/learn/advanced-sql). +- **Python for Analysis:** + - Book: *"Python for Data Analysis"* by Wes McKinney. + - Tutorial: [Kaggle’s Time-Series Course](https://www.kaggle.com/learn/time-series). +- **Probability:** + - Book: *"Fifty Challenging Problems in Probability"* by Mosteller. + +--- + +### **Key Insight** +Lottery data is perfect for OLAP because: +- It’s **append-only** (historical, no updates). +- Queries are **analytical** (e.g., "What’s the trend?" not "What’s today’s number?"). +- You’ll learn **time dimensions** (day/week/month) and **aggregations** (counts, averages). + +--- + +Great question! The choice between **normalized (3NF)** and **denormalized (star schema)** data models depends on whether you're optimizing for **OLTP (transaction processing)** or **OLAP (analytics)**. Let’s break it down: + +--- + +### **1. Normalized Data Model (3NF – Third Normal Form)** +**Used in:** OLTP systems (e.g., MySQL, PostgreSQL for transactional apps). +**Goal:** Minimize redundancy, ensure data integrity, and optimize for fast writes. + +#### **Key Features:** +- **Split into multiple related tables** (eliminates duplicate data). +- Uses **foreign keys** to enforce relationships. +- Follows **normalization rules** (1NF, 2NF, 3NF, etc.). + +#### **Example (E-commerce OLTP Database):** +```plaintext +Customers Table Orders Table Products Table ++------------+ +-----------+ +------------+ +| CustomerID | | OrderID | | ProductID | +| Name | | CustomerID| | Name | +| Email | | OrderDate | | Price | ++------------+ +-----------+ +------------+ + +Order_Details Table (Junction Table) ++-------------------+ +| OrderDetailID | +| OrderID | +| ProductID | +| Quantity | ++-------------------+ +``` +- **Normalized (3NF)**: No duplicate data; updates are efficient. +- **Downside for Analytics**: Complex joins slow down queries. + +--- + +### **2. Denormalized Data Model (Star Schema)** +**Used in:** OLAP systems (e.g., data warehouses like Snowflake, Redshift). +**Goal:** Optimize for fast reads, reduce joins, and speed up analytical queries. + +#### **Key Features:** +- **Central fact table** (stores metrics like sales, revenue). +- **Surrounded by dimension tables** (descriptive attributes like time, product, customer). +- **Redundant data** (denormalized for faster queries). + +#### **Example (Sales Data Warehouse):** +```plaintext +Fact_Sales (Fact Table) ++-----------+-----------+-----------+------------+ +| SaleID | ProductID | CustomerID| TimeID | +| Quantity | Revenue | Discount | Profit | ++-----------+-----------+-----------+------------+ + +Dim_Product (Dimension Table) ++-----------+-----------+-----------+ +| ProductID | Name | Category | ++-----------+-----------+-----------+ + +Dim_Customer (Dimension Table) ++-----------+-----------+-----------+ +| CustomerID| Name | Region | ++-----------+-----------+-----------+ + +Dim_Time (Dimension Table) ++-----------+-----------+-----------+ +| TimeID | Date | Quarter | ++-----------+-----------+-----------+ +``` +- **Denormalized (Star Schema)**: Fewer joins, faster for analytics. +- **Downside for OLTP**: Redundant data, harder to update. + +--- + +### **Comparison Table** +| Feature | Normalized (3NF) | Denormalized (Star Schema) | +|----------------------|-------------------------------------|-------------------------------------| +| **Structure** | Many tables, linked via foreign keys | Few tables (fact + dimensions) | +| **Data Redundancy** | Minimal (normalized) | High (denormalized for speed) | +| **Query Performance** | Slower for analytics (many joins) | Faster for analytics (fewer joins) | +| **Write Performance** | Fast (optimized for OLTP) | Slow (batch updates in OLAP) | +| **Use Case** | OLTP (banking, e-commerce) | OLAP (BI, reporting, dashboards) | +| **Example Databases** | MySQL, PostgreSQL (transactional) | Snowflake, Redshift (data warehouse)| + +--- + +### **When to Use Which?** +- **Use Normalized (3NF) if:** + - You need **ACID compliance** (transactions must be reliable). + - Your app is **write-heavy** (e.g., order processing). + - Data integrity is critical (no duplicates). + +- **Use Denormalized (Star Schema) if:** + - You need **fast analytical queries**. + - Your system is **read-heavy** (e.g., BI tools like Power BI). + - You’re working with **historical data** (not real-time updates). + +--- + +### **Real-World Analogy** +- **Normalized (OLTP)** = A **library’s database** where each book, author, and borrower is stored separately (efficient for updates). +- **Denormalized (OLAP)** = A **summary report** where book sales, author info, and time trends are merged for quick analysis. + +Would you like a deeper dive into **snowflake schema** (a variant of star schema) or how ETL pipelines transform normalized OLTP data into denormalized OLAP formats? \ No newline at end of file