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SQL basics
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‎Advanced Level.sql

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-- Advanced Level SQL Script Guide
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-- Step 1: Advanced Database Administration
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-- Example 1: Create a database backup
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-- Note: Database backup commands can vary depending on the DBMS used.
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-- For MySQL, use: mysqldump -u username -p database_name > backup_file.sql
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-- Example 2: Restore a database from a backup
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-- Note: Restore commands can vary depending on the DBMS used.
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-- For MySQL, use: mysql -u username -p database_name < backup_file.sql
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-- Step 2: Database Performance Optimization
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-- Example: Analyze and optimize slow queries using indexes, table partitioning, and query rewriting.
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-- Step 3: High Availability and Replication
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-- Example: Set up database replication for fault tolerance and scalability.
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-- Step 4: Working with Large Databases
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-- Example 1: Create a partitioned table for faster data retrieval
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CREATE TABLE books_partitioned (
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book_id INT PRIMARY KEY,
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title VARCHAR(100),
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author_id INT,
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price DECIMAL(8,2)
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)
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PARTITION BY RANGE (book_id)(
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PARTITION p0 VALUES LESS THAN (100),
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PARTITION p1 VALUES LESS THAN (200),
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PARTITION p2 VALUES LESS THAN (MAXVALUE)
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);
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-- Example 2: Implement Sharding for distributing data across multiple servers
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-- Note: Sharding is a complex technique and depends on the specific DBMS and setup.
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-- Step 5: Advanced SQL Techniques
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-- Example 1: Recursive SQL for hierarchical data (revisited)
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WITH RECURSIVE category_tree AS (
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SELECT category_id, category_name, parent_category_id
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FROM categories
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WHERE category_id = 1
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UNION ALL
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SELECT c.category_id, c.category_name, c.parent_category_id
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FROM categories c
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JOIN category_tree ct ON c.parent_category_id = ct.category_id
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)
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SELECT * FROM category_tree;
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-- Example 2: Pivoting Data using CASE and Aggregate functions
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SELECT
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author_id,
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SUM(CASE WHEN price > 20.00 THEN 1 ELSE 0 END) AS expensive_books,
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SUM(CASE WHEN price <= 20.00 THEN 1 ELSE 0 END) AS cheap_books
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FROM books
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GROUP BY author_id;
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-- Step 6: Incorporating Machine Learning Models with SQL
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-- Example: Use a machine learning model to predict book prices based on other features.
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-- Step 7: Advanced Data Analysis
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-- Example 1: Use advanced statistical functions for data analysis
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SELECT
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author_id,
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AVG(price) AS avg_price,
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STDDEV(price) AS price_stddev
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FROM books
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GROUP BY author_id;
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-- Example 2: Use the LEAD and LAG functions for time-series analysis
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SELECT
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book_id,
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title,
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price,
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LAG(price) OVER (ORDER BY book_id) AS prev_price,
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LEAD(price) OVER (ORDER BY book_id) AS next_price
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FROM books;
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-- Step 8: Real-World Projects
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-- Work on complex database projects involving big data, data warehousing, and analytics.
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-- Example: Data Warehousing (Continued from Step 8)
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-- Assume you're working on a data warehousing project for an e-commerce company.
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-- Create a data warehouse schema for online sales data.
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CREATE SCHEMA data_warehouse;
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CREATE TABLE data_warehouse.sales (
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sale_id INT PRIMARY KEY,
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sale_date DATE NOT NULL,
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customer_id INT NOT NULL,
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product_id INT NOT NULL,
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quantity INT NOT NULL,
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total_price DECIMAL(10, 2) NOT NULL
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);
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-- Implement ETL processes to populate the data warehouse.
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INSERT INTO data_warehouse.sales (sale_id, sale_date, customer_id, product_id, quantity, total_price)
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SELECT
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s.sale_id,
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s.sale_date,
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u.user_id AS customer_id,
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p.product_id,
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s.quantity,
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s.quantity * p.price AS total_price
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FROM online_sales.sales s
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JOIN users u ON s.username = u.username
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JOIN products p ON s.product_name = p.product_name;
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-- Task 9: Advanced Security Measures
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-- Implement encryption, authentication, and authorization for securing sensitive data.
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-- Example: Data Encryption (Continued from Task 9)
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-- Suppose you're working for a healthcare organization managing a database containing sensitive patient information.
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-- Create a table to store patient records with encrypted data.
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CREATE TABLE patient_records (
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record_id INT PRIMARY KEY,
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patient_name VARBINARY(100) NOT NULL,
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medical_history VARBINARY(200),
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prescription VARBINARY(500)
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);
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-- Implement data encryption using SQL's cryptographic functions.
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INSERT INTO patient_records (record_id, patient_name, medical_history, prescription)
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VALUES
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(1, AES_ENCRYPT('John Doe', 'encryption_key'), AES_ENCRYPT('Heart condition', 'encryption_key'), AES_ENCRYPT('Medication X', 'encryption_key')),
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(2, AES_ENCRYPT('Jane Smith', 'encryption_key'), AES_ENCRYPT('Allergy to penicillin', 'encryption_key'), AES_ENCRYPT('Medication Y', 'encryption_key'));
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-- Task 10: Integration of SQL and NoSQL Databases (Continued from Task 10)
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-- Assume you have an SQL database for user information and a NoSQL database for user-generated content.
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-- Example: Querying and combining data from SQL and NoSQL databases (Continued from Task 10)
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-- Retrieve user information along with their latest post (from NoSQL) if available.
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SELECT
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u.user_id,
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u.username,
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u.email,
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u.registration_date,
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p.post_id,
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p.content AS latest_post
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FROM users u
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LEFT JOIN (
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SELECT user_id, MAX(post_id) AS post_id
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FROM user_posts.posts
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GROUP BY user_id
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) max_posts ON u.user_id = max_posts.user_id
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LEFT JOIN user_posts.posts p ON max_posts.post_id = p.post_id;
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-- Step 10: Integration of SQL and NoSQL databases
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-- Example: Combining SQL and NoSQL Data
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-- Assume you have an SQL database with 'users' table containing user information.
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CREATE TABLE users (
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user_id INT PRIMARY KEY,
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username VARCHAR(50) NOT NULL,
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email VARCHAR(100) NOT NULL,
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registration_date DATE NOT NULL
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);
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INSERT INTO users (user_id, username, email, registration_date)
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VALUES
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(1, 'john_doe', 'john@example.com', '2023年07月01日'),
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(2, 'jane_smith', 'jane@example.com', '2023年07月02日');
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-- Assume you have a NoSQL database storing user-generated content (e.g., posts) as JSON documents.
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-- Let's assume the NoSQL database is named 'user_posts' and the JSON documents are stored in a 'posts' collection.
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-- Example: Querying and combining data from SQL and NoSQL databases
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-- Retrieve user information along with their latest post (from NoSQL) if available.
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SELECT
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u.user_id,
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u.username,
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u.email,
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u.registration_date,
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p.post_id,
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p.content AS latest_post
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FROM users u
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LEFT JOIN (
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SELECT user_id, MAX(post_id) AS post_id
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FROM user_posts.posts
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GROUP BY user_id
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) max_posts ON u.user_id = max_posts.user_id
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LEFT JOIN user_posts.posts p ON max_posts.post_id = p.post_id;
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-- Step 11: Real-Time Data Processing
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-- Example: Real-time analytics with streaming data
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-- Assume you have a table for real-time transactions in an SQL database.
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CREATE TABLE real_time_transactions (
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transaction_id INT PRIMARY KEY,
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user_id INT NOT NULL,
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transaction_date TIMESTAMP NOT NULL,
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amount DECIMAL(10, 2) NOT NULL
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);
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-- Example: Streaming Analytics
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-- Suppose you want to monitor real-time transactions and raise an alert for unusually large transactions.
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-- Create a trigger to handle real-time data processing when a new transaction is inserted.
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DELIMITER //
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CREATE TRIGGER process_real_time_transactions
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AFTER INSERT ON real_time_transactions
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FOR EACH ROW
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BEGIN
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DECLARE avg_amount DECIMAL(10, 2);
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DECLARE std_amount DECIMAL(10, 2);
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SELECT AVG(amount), STDDEV(amount) INTO avg_amount, std_amount FROM real_time_transactions;
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IF NEW.amount > (avg_amount + 2 * std_amount) THEN
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INSERT INTO transaction_alerts (transaction_id, user_id, alert_message)
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VALUES (NEW.transaction_id, NEW.user_id, 'Unusually large transaction detected.');
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END IF;
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END;
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//
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DELIMITER ;
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-- The trigger will analyze each new transaction and insert an entry in the 'transaction_alerts' table if the transaction amount is significantly larger than the average amount of all transactions.
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-- Note: These are simplified examples to demonstrate the concepts. Real-world implementations may vary based on the specific database systems and requirements.
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-- Step 12: Advanced Optimization Techniques
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-- Example: Query Optimization with Materialized Views
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-- Assume you have a table 'orders' and want to create a materialized view for faster querying.
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CREATE TABLE orders (
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order_id INT PRIMARY KEY,
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customer_id INT NOT NULL,
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order_date DATE NOT NULL,
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total_amount DECIMAL(10, 2) NOT NULL
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);
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-- Create a materialized view to store aggregated order data for faster retrieval.
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CREATE MATERIALIZED VIEW mv_order_summary AS
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SELECT
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customer_id,
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COUNT(order_id) AS order_count,
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SUM(total_amount) AS total_sales
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FROM orders
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GROUP BY customer_id;
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-- Refresh the materialized view periodically to keep it up-to-date with the underlying data.
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REFRESH MATERIALIZED VIEW mv_order_summary;
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-- Step 13: Scalability and Load Balancing
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-- Example: Implementing Sharding for Horizontal Scaling
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-- Assume you have a table 'user_data' and want to shard it across multiple servers.
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CREATE TABLE user_data (
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user_id INT PRIMARY KEY,
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username VARCHAR(50) NOT NULL,
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email VARCHAR(100) NOT NULL
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);
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-- Create a sharded table by distributing data based on the user_id range.
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-- Note: Sharding logic depends on the specific requirements and DBMS used.
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CREATE TABLE user_data_shard1 (LIKE user_data INCLUDING ALL) TABLESPACE ts1;
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CREATE TABLE user_data_shard2 (LIKE user_data INCLUDING ALL) TABLESPACE ts2;
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-- ... Create more shards as needed.
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-- Implement a sharding mechanism to distribute data across shards based on user_id.
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-- Step 14: Blockchain and Distributed Ledger Integration
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-- Example: Interacting with Blockchain Networks
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-- Assume you have a table 'blockchain_data' to store data related to blockchain transactions.
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CREATE TABLE blockchain_data (
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transaction_id INT PRIMARY KEY,
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sender_address VARCHAR(100) NOT NULL,
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receiver_address VARCHAR(100) NOT NULL,
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amount DECIMAL(10, 2) NOT NULL
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);
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-- Insert data into the 'blockchain_data' table to represent blockchain transactions.
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-- Example: Query blockchain data to calculate the total transaction amount for a specific sender.
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SELECT
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sender_address,
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SUM(amount) AS total_sent_amount
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FROM blockchain_data
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WHERE sender_address = '0xabc...'; -- Replace with the actual sender address
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GROUP BY sender_address;
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-- Step 15: Real-World Projects
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-- Continue working on complex projects to deepen your SQL expertise.
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-- Example: Large-Scale Data Migration
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-- In a real-world project, you might encounter a scenario where a company wants to migrate its data from an old database system to a new one. You'll use SQL to design and execute the data migration process, ensuring data integrity and minimal downtime during the transition.
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-- For Step 15, the example involves real-world projects, which can vary significantly based on specific requirements and business domains.
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-- Congratulations! You have completed the Advanced Level SQL Script Guide.

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