# Apache Spark
from pyspark.sql import SparkSession

# Create a SparkSession
spark = SparkSession.builder \
    .appName("ETL with Spark") \
    .getOrCreate()

# Read data from a file
df = spark.read.csv("data.csv", header=True, inferSchema=True)

# Perform transformations
df_transformed = df.filter(df.age > 30).groupBy("gender").count()

# Write data to a file
df_transformed.write.csv("output.csv")

# Talend
import talend

# Create a Talend job
job = talend.Job()

# Extract data from a database
job.extract(source="Database", table="employees", target="data.csv")

# Transform data
job.transform(data="data.csv", transformation="filter_data.talend")

# Load data to a data warehouse
job.load(target="DataWarehouse", table="employees_transformed")

# Execute the Talend job
job.execute()

# Pentaho Data Integration
import pentaho

# Create a PDI transformation
transformation = pentaho.Transformation()

# Extract data from a database
transformation.extract(source="Database", query="SELECT * FROM employees", target="data.csv")

# Transform data
transformation.transform(data="data.csv", transformation="filter_data.ktr")

# Load data to a data warehouse
transformation.load(target="DataWarehouse", table="employees_transformed")

# Execute the PDI transformation
transformation.execute()

import pandas as pd

# Simulating the extracted data
extracted_data = pd.DataFrame({
    'id': [1, 2, 3, 4, 5],
    'name': ['John', 'Alice', 'Bob', 'Jane', 'Emma'],
    'age': [25, 28, 30, 35, 30],
    'city': ['New York', 'London', 'Paris', 'Tokyo', 'Sydney']
})

# Transform the extracted data
def transform_data(data):
    # Perform data cleaning, transformation, and normalization
    transformed_data = data.copy()
    
    # Example transformation: Add 1 to age
    transformed_data['age'] = transformed_data['age'] + 1
    
    return transformed_data

# Load the transformed data
def load_data(data):
    # Simulating the loading of data into a destination system
    # Here, we simply print the loaded data as the result
    print("Loaded Data:")
    print(data)
    print()

# Perform ETL steps
transformed_data = transform_data(extracted_data)
load_data(transformed_data)

import pandas as pd

# Simulating the raw data
raw_data = pd.DataFrame({
    'id': [1, 2, 3, None, 5],
    'name': ['John', 'Alice', 'Bob', 'Jane', 'Emma'],
    'age': [25, None, 30, 35, 30],
    'gender': ['M', 'F', 'M', 'F', 'F']
})

# Clean the data
def clean_data(data):
    # Check for missing or null values
    data_cleaned = data.dropna()
    
    return data_cleaned

# Imputation
def impute_data(data):
    # Use imputation methods to fill in missing or null values
    data_imputed = data.fillna({
        'id': 0,
        'name': 'Unknown',
        'age': data['age'].mean()
    })
    
    return data_imputed

# Verify the data and sources
def verify_data(data):
    # Check and verify the sources of data
    # Here, we are simply printing the data for verification
    print("Verified Data:")
    print(data)
    print()

# Sort the data
def sort_data(data, sort_by):
    # Sort the data based on a specified column
    data_sorted = data.sort_values(by=sort_by)
    
    return data_sorted

# Standardize and remove duplicates
def standardize_and_remove_duplicates(data):
    # Standardize the data and remove duplicates
    data_standardized = data.apply(lambda x: x.str.upper() if x.dtype == 'object' else x)
    data_deduplicated = data_standardized.drop_duplicates()
    
    return data_deduplicated

# Perform ETL steps
cleaned_data = clean_data(raw_data)
imputed_data = impute_data(cleaned_data)
verify_data(imputed_data)
sorted_data = sort_data(imputed_data, 'age')
final_data = standardize_and_remove_duplicates(sorted_data)

# Print the final processed data
print("Processed Data:")
print(final_data)

import pandas as pd

# Simulating new data to be loaded
new_data = pd.DataFrame({
    'id': [101, 102, 103],
    'name': ['John', 'Alice', 'Bob'],
    'age': [25, 30, 28]
})

# Simulating existing data in the data warehouse
existing_data = pd.DataFrame({
    'id': [101, 102, 103, 104],
    'name': ['John', 'Alice', 'Bob', 'Emma'],
    'age': [25, 30, 28, 35]
})

# Incremental Load
def incremental_load(existing_data, new_data):
    # Check for new and unique rows
    new_rows = new_data[~new_data['id'].isin(existing_data['id'])]
    
    # Append new rows to the existing data
    updated_data = existing_data.append(new_rows)
    
    return updated_data

# Perform incremental load
updated_data = incremental_load(existing_data, new_data)
print("Data after incremental load:")
print(updated_data)
print()

# Full Load
def full_load(existing_data, new_data):
    # Replace the existing data with the new data
    updated_data = new_data
    
    return updated_data

# Perform full load
updated_data = full_load(existing_data, new_data)
print("Data after full load:")
print(updated_data)

-- Basic Functions --

-- Date and Time Functions
SELECT CURRENT_DATE();
SELECT CURRENT_TIMESTAMP();

-- String Functions
SELECT LENGTH('Snowflake');
SELECT SUBSTRING('Snowflake', 1, 4);
SELECT CONCAT('Hello', ' ', 'World');

-- Numeric Functions
SELECT ABS(-10);
SELECT ROUND(3.14159, 2);
SELECT FLOOR(5.8);

-- Advanced Functions --

-- Aggregate Functions
SELECT COUNT(*) FROM table_name;
SELECT SUM(sales_amount) FROM sales_table;
SELECT AVG(price) FROM products_table;

-- Window Functions
SELECT customer_id, order_date, order_amount, 
       SUM(order_amount) OVER (PARTITION BY customer_id ORDER BY order_date) AS cumulative_amount
FROM orders_table;

-- String Manipulation Functions
SELECT LOWER('SNOWFLAKE');
SELECT UPPER('snowflake');
SELECT TRIM('   Snowflake   ');

-- Date and Time Functions
SELECT DATEADD('day', 7, '2023-05-21');
SELECT DATEDIFF('day', '2023-05-21', '2023-06-01');

-- Conditional Functions
SELECT CASE WHEN order_amount > 1000 THEN 'High' ELSE 'Low' END AS order_category
FROM orders_table;

-- JSON Functions
SELECT json_value(customer_data, '$.name') AS customer_name
FROM customers_table;

-- Table Functions
SELECT *
FROM TABLE(FLATTEN(input_table, 'json_path'));

-- Geospatial Functions
SELECT ST_DISTANCE(ST_POINT(0, 0), ST_POINT(1, 1));

-- Analytic Functions
SELECT product_name, sales_amount, 
       RANK() OVER (PARTITION BY product_category ORDER BY sales_amount DESC) AS rank
FROM sales_table;