Advanced Level.sql

-- Advanced Level SQL Script Guide

-- Step 1: Advanced Database Administration
-- Example 1: Create a database backup
-- Note: Database backup commands can vary depending on the DBMS used.
-- For MySQL, use: mysqldump -u username -p database_name > backup_file.sql

-- Example 2: Restore a database from a backup
-- Note: Restore commands can vary depending on the DBMS used.
-- For MySQL, use: mysql -u username -p database_name < backup_file.sql

-- Step 2: Database Performance Optimization
-- Example: Analyze and optimize slow queries using indexes, table partitioning, and query rewriting.

-- Step 3: High Availability and Replication
-- Example: Set up database replication for fault tolerance and scalability.

-- Step 4: Working with Large Databases
-- Example 1: Create a partitioned table for faster data retrieval
CREATE TABLE books_partitioned (
    book_id INT PRIMARY KEY,
    title VARCHAR(100),
    author_id INT,
    price DECIMAL(8,2)
)
PARTITION BY RANGE (book_id)(
    PARTITION p0 VALUES LESS THAN (100),
    PARTITION p1 VALUES LESS THAN (200),
    PARTITION p2 VALUES LESS THAN (MAXVALUE)
);

-- Example 2: Implement Sharding for distributing data across multiple servers
-- Note: Sharding is a complex technique and depends on the specific DBMS and setup.

-- Step 5: Advanced SQL Techniques
-- Example 1: Recursive SQL for hierarchical data (revisited)
WITH RECURSIVE category_tree AS (
    SELECT category_id, category_name, parent_category_id
    FROM categories
    WHERE category_id = 1
    UNION ALL
    SELECT c.category_id, c.category_name, c.parent_category_id
    FROM categories c
    JOIN category_tree ct ON c.parent_category_id = ct.category_id
)
SELECT * FROM category_tree;

-- Example 2: Pivoting Data using CASE and Aggregate functions
SELECT
    author_id,
    SUM(CASE WHEN price > 20.00 THEN 1 ELSE 0 END) AS expensive_books,
    SUM(CASE WHEN price <= 20.00 THEN 1 ELSE 0 END) AS cheap_books
FROM books
GROUP BY author_id;

-- Step 6: Incorporating Machine Learning Models with SQL
-- Example: Use a machine learning model to predict book prices based on other features.

-- Step 7: Advanced Data Analysis
-- Example 1: Use advanced statistical functions for data analysis
SELECT
    author_id,
    AVG(price) AS avg_price,
    STDDEV(price) AS price_stddev
FROM books
GROUP BY author_id;

-- Example 2: Use the LEAD and LAG functions for time-series analysis
SELECT
    book_id,
    title,
    price,
    LAG(price) OVER (ORDER BY book_id) AS prev_price,
    LEAD(price) OVER (ORDER BY book_id) AS next_price
FROM books;

-- Step 8: Real-World Projects
-- Work on complex database projects involving big data, data warehousing, and analytics.

-- Example: Data Warehousing (Continued from Step 8)
-- Assume you're working on a data warehousing project for an e-commerce company.

-- Create a data warehouse schema for online sales data.
CREATE SCHEMA data_warehouse;

CREATE TABLE data_warehouse.sales (
    sale_id INT PRIMARY KEY,
    sale_date DATE NOT NULL,
    customer_id INT NOT NULL,
    product_id INT NOT NULL,
    quantity INT NOT NULL,
    total_price DECIMAL(10, 2) NOT NULL
);

-- Implement ETL processes to populate the data warehouse.
INSERT INTO data_warehouse.sales (sale_id, sale_date, customer_id, product_id, quantity, total_price)
SELECT
    s.sale_id,
    s.sale_date,
    u.user_id AS customer_id,
    p.product_id,
    s.quantity,
    s.quantity * p.price AS total_price
FROM online_sales.sales s
JOIN users u ON s.username = u.username
JOIN products p ON s.product_name = p.product_name;

-- Task 9: Advanced Security Measures
-- Implement encryption, authentication, and authorization for securing sensitive data.

-- Example: Data Encryption (Continued from Task 9)
-- Suppose you're working for a healthcare organization managing a database containing sensitive patient information.

-- Create a table to store patient records with encrypted data.
CREATE TABLE patient_records (
    record_id INT PRIMARY KEY,
    patient_name VARBINARY(100) NOT NULL,
    medical_history VARBINARY(200),
    prescription VARBINARY(500)
);

-- Implement data encryption using SQL's cryptographic functions.
INSERT INTO patient_records (record_id, patient_name, medical_history, prescription)
VALUES
    (1, AES_ENCRYPT('John Doe', 'encryption_key'), AES_ENCRYPT('Heart condition', 'encryption_key'), AES_ENCRYPT('Medication X', 'encryption_key')),
    (2, AES_ENCRYPT('Jane Smith', 'encryption_key'), AES_ENCRYPT('Allergy to penicillin', 'encryption_key'), AES_ENCRYPT('Medication Y', 'encryption_key'));

-- Task 10: Integration of SQL and NoSQL Databases (Continued from Task 10)
-- Assume you have an SQL database for user information and a NoSQL database for user-generated content.

-- Example: Querying and combining data from SQL and NoSQL databases (Continued from Task 10)
-- Retrieve user information along with their latest post (from NoSQL) if available.

SELECT
    u.user_id,
    u.username,
    u.email,
    u.registration_date,
    p.post_id,
    p.content AS latest_post
FROM users u
LEFT JOIN (
    SELECT user_id, MAX(post_id) AS post_id
    FROM user_posts.posts
    GROUP BY user_id
) max_posts ON u.user_id = max_posts.user_id
LEFT JOIN user_posts.posts p ON max_posts.post_id = p.post_id;

-- Step 10: Integration of SQL and NoSQL databases
-- Example: Combining SQL and NoSQL Data

-- Assume you have an SQL database with 'users' table containing user information.
CREATE TABLE users (
    user_id INT PRIMARY KEY,
    username VARCHAR(50) NOT NULL,
    email VARCHAR(100) NOT NULL,
    registration_date DATE NOT NULL
);

INSERT INTO users (user_id, username, email, registration_date)
VALUES
    (1, 'john_doe', 'john@example.com', '2023-07-01'),
    (2, 'jane_smith', 'jane@example.com', '2023-07-02');

-- Assume you have a NoSQL database storing user-generated content (e.g., posts) as JSON documents.
-- Let's assume the NoSQL database is named 'user_posts' and the JSON documents are stored in a 'posts' collection.

-- Example: Querying and combining data from SQL and NoSQL databases
-- Retrieve user information along with their latest post (from NoSQL) if available.

SELECT
    u.user_id,
    u.username,
    u.email,
    u.registration_date,
    p.post_id,
    p.content AS latest_post
FROM users u
LEFT JOIN (
    SELECT user_id, MAX(post_id) AS post_id
    FROM user_posts.posts
    GROUP BY user_id
) max_posts ON u.user_id = max_posts.user_id
LEFT JOIN user_posts.posts p ON max_posts.post_id = p.post_id;

-- Step 11: Real-Time Data Processing
-- Example: Real-time analytics with streaming data

-- Assume you have a table for real-time transactions in an SQL database.
CREATE TABLE real_time_transactions (
    transaction_id INT PRIMARY KEY,
    user_id INT NOT NULL,
    transaction_date TIMESTAMP NOT NULL,
    amount DECIMAL(10, 2) NOT NULL
);

-- Example: Streaming Analytics
-- Suppose you want to monitor real-time transactions and raise an alert for unusually large transactions.

-- Create a trigger to handle real-time data processing when a new transaction is inserted.
DELIMITER //
CREATE TRIGGER process_real_time_transactions
AFTER INSERT ON real_time_transactions
FOR EACH ROW
BEGIN
    DECLARE avg_amount DECIMAL(10, 2);
    DECLARE std_amount DECIMAL(10, 2);
    SELECT AVG(amount), STDDEV(amount) INTO avg_amount, std_amount FROM real_time_transactions;
    IF NEW.amount > (avg_amount + 2 * std_amount) THEN
        INSERT INTO transaction_alerts (transaction_id, user_id, alert_message)
        VALUES (NEW.transaction_id, NEW.user_id, 'Unusually large transaction detected.');
    END IF;
END;
//
DELIMITER ;

-- 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.

-- Note: These are simplified examples to demonstrate the concepts. Real-world implementations may vary based on the specific database systems and requirements.

-- Step 12: Advanced Optimization Techniques
-- Example: Query Optimization with Materialized Views

-- Assume you have a table 'orders' and want to create a materialized view for faster querying.
CREATE TABLE orders (
    order_id INT PRIMARY KEY,
    customer_id INT NOT NULL,
    order_date DATE NOT NULL,
    total_amount DECIMAL(10, 2) NOT NULL
);

-- Create a materialized view to store aggregated order data for faster retrieval.
CREATE MATERIALIZED VIEW mv_order_summary AS
SELECT
    customer_id,
    COUNT(order_id) AS order_count,
    SUM(total_amount) AS total_sales
FROM orders
GROUP BY customer_id;

-- Refresh the materialized view periodically to keep it up-to-date with the underlying data.
REFRESH MATERIALIZED VIEW mv_order_summary;

-- Step 13: Scalability and Load Balancing
-- Example: Implementing Sharding for Horizontal Scaling

-- Assume you have a table 'user_data' and want to shard it across multiple servers.
CREATE TABLE user_data (
    user_id INT PRIMARY KEY,
    username VARCHAR(50) NOT NULL,
    email VARCHAR(100) NOT NULL
);

-- Create a sharded table by distributing data based on the user_id range.
-- Note: Sharding logic depends on the specific requirements and DBMS used.
CREATE TABLE user_data_shard1 (LIKE user_data INCLUDING ALL) TABLESPACE ts1;
CREATE TABLE user_data_shard2 (LIKE user_data INCLUDING ALL) TABLESPACE ts2;
-- ... Create more shards as needed.

-- Implement a sharding mechanism to distribute data across shards based on user_id.

-- Step 14: Blockchain and Distributed Ledger Integration
-- Example: Interacting with Blockchain Networks

-- Assume you have a table 'blockchain_data' to store data related to blockchain transactions.
CREATE TABLE blockchain_data (
    transaction_id INT PRIMARY KEY,
    sender_address VARCHAR(100) NOT NULL,
    receiver_address VARCHAR(100) NOT NULL,
    amount DECIMAL(10, 2) NOT NULL
);

-- Insert data into the 'blockchain_data' table to represent blockchain transactions.

-- Example: Query blockchain data to calculate the total transaction amount for a specific sender.
SELECT
    sender_address,
    SUM(amount) AS total_sent_amount
FROM blockchain_data
WHERE sender_address = '0xabc...'; -- Replace with the actual sender address
GROUP BY sender_address;

-- Step 15: Real-World Projects
-- Continue working on complex projects to deepen your SQL expertise.

-- Example: Large-Scale Data Migration
-- 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.

-- For Step 15, the example involves real-world projects, which can vary significantly based on specific requirements and business domains.

-- Congratulations! You have completed the Advanced Level SQL Script Guide.