import pandas as pd
from pyspark.sql import SparkSession

# Create a SparkSession
spark = SparkSession.builder.master("local").appName("BigDataExample").getOrCreate()

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

# Show the first few rows of the data
df.show(5)

# Perform data transformations and analysis
# ...

# Filter data based on specific conditions
filtered_df = df.filter(df['age'] > 30)

# Aggregate data using groupBy and aggregation functions
grouped_df = df.groupBy('gender').agg({'age': 'mean', 'income': 'sum'})

# Export data to a new format (e.g., Parquet, Avro)
df.write.parquet("big_data.parquet")

# Load data from a database or API (depending on the data source)
df_from_db = spark.read.jdbc(url="jdbc:postgresql://localhost:5432/mydb", table="mytable")

# Write data to a database or API (depending on the data destination)
df.write.jdbc(url="jdbc:postgresql://localhost:5432/mydb", table="mytable", mode="append")

# Perform complex analytics using Spark's machine learning libraries
from pyspark.ml.regression import LinearRegression

# Define features and target column
features = df.select('age', 'income')
target = df.select('savings')

# Fit a linear regression model
lr = LinearRegression(featuresCol='features', labelCol='label')
model = lr.fit(features, target)

# Make predictions on new data
new_data = spark.createDataFrame([(35, 50000)], ['age', 'income'])
predictions = model.transform(new_data)

# Show the predictions
predictions.show()

from cassandra.cluster import Cluster

# Connect to Cassandra cluster
cluster = Cluster(['127.0.0.1'])
session = cluster.connect()

# Create a keyspace
session.execute("CREATE KEYSPACE IF NOT EXISTS my_keyspace WITH replication = {'class': 'SimpleStrategy', 'replication_factor': '1'}")

# Create a table
session.execute("CREATE TABLE IF NOT EXISTS my_keyspace.my_table (id UUID PRIMARY KEY, name TEXT)")

# Insert data into the table
session.execute("INSERT INTO my_keyspace.my_table (id, name) VALUES (uuid(), 'John Doe')")

# Retrieve data from the table
result = session.execute("SELECT * FROM my_keyspace.my_table")
for row in result:
    print(row.id, row.name)

# Close the session and cluster
session.shutdown()
cluster.shutdown()

from pyspark.sql import SparkSession

# Create SparkSession
spark = SparkSession.builder.appName("Real-time Processing").getOrCreate()

# Read data from a streaming source
df = spark.readStream.format("csv").option("header", "true").load("input.csv")

# Perform transformations on the streaming data
transformed_df = df.select("name", "age").filter("age >= 18")

# Write the transformed data to an output sink
query = transformed_df.writeStream.format("console").outputMode("append").start()

# Start the streaming query
query.awaitTermination()

# Stop the SparkSession
spark.stop()

from kafka import KafkaProducer, KafkaConsumer

# Create a Kafka producer
producer = KafkaProducer(bootstrap_servers='localhost:9092')

# Send a message to a Kafka topic
producer.send('my_topic', b'Hello, Kafka!')

# Create a Kafka consumer
consumer = KafkaConsumer('my_topic', bootstrap_servers='localhost:9092')

# Consume messages from the Kafka topic
for message in consumer:
    print(message.value)

# Close the Kafka producer and consumer
producer.close()
consumer.close()

from pyhive import hive

# Connect to Hive server
connection = hive.connect(host='localhost', port=10000, username='hiveuser')

# Create a Hive table
cursor = connection.cursor()
cursor.execute("CREATE TABLE IF NOT EXISTS my_table (id INT, name STRING)")

# Insert data into the table
cursor.execute("INSERT INTO my_table VALUES (1, 'John Doe')")

# Query data from the table
cursor.execute("SELECT * FROM my_table")
result = cursor.fetchall()
for row in result:
    print(row)

# Close the cursor and connection
cursor.close()
connection.close()

# Mapper (map.py)
import sys

# Iterate over each line of input
for line in sys.stdin:
    # Split the line into fields
    fields = line.strip().split(',')

    # Extract the customer and revenue
    customer = fields[0]
    revenue = float(fields[2])

    # Emit key-value pair: customer as key and revenue as value
    print(customer + '\t' + str(revenue))

# Reducer (reduce.py)
import sys

current_customer = None
total_revenue = 0.0

# Iterate over each line of input
for line in sys.stdin:
    # Split the line into key and value
    customer, revenue = line.strip().split('\t')

    # Convert revenue to float
    revenue = float(revenue)

    # If it's the same customer, accumulate the revenue
    if current_customer == customer:
        total_revenue += revenue
    # If it's a new customer, emit the total revenue for the previous customer
    else:
        if current_customer:
            print(current_customer + '\t' + str(total_revenue))
        current_customer = customer
        total_revenue = revenue

# Emit the total revenue for the last customer
if current_customer:
    print(current_customer + '\t' + str(total_revenue))

-- Load data from HDFS
data = LOAD '/path/to/input/data' USING PigStorage(',');

-- Filter data
filtered_data = FILTER data BY age >= 18;

-- Group data by gender
grouped_data = GROUP filtered_data BY gender;

-- Calculate average age per gender
avg_age = FOREACH grouped_data GENERATE group AS gender, AVG(filtered_data.age) AS avg_age;

-- Store the result in HDFS
STORE avg_age INTO '/path/to/output/result' USING PigStorage(',');

# SQL Database (SQLite)
import sqlite3

# Create a connection to the database
conn = sqlite3.connect('employees.db')

# Create a cursor object to execute SQL queries
cursor = conn.cursor()

# Create a table
cursor.execute('''CREATE TABLE Employees (
                    id INTEGER PRIMARY KEY,
                    name TEXT,
                    age INTEGER,
                    department TEXT
                )''')

# Insert data into the table
cursor.execute("INSERT INTO Employees VALUES (1, 'John Doe', 30, 'Sales')")
cursor.execute("INSERT INTO Employees VALUES (2, 'Jane Smith', 35, 'Marketing')")
cursor.execute("INSERT INTO Employees VALUES (3, 'Mike Johnson', 40, 'IT')")

# Retrieve data from the table
cursor.execute("SELECT * FROM Employees WHERE department = 'Sales'")
rows = cursor.fetchall()

# Print the retrieved data
for row in rows:
    print(row)

# Close the connection
conn.close()


# NoSQL Database (MongoDB)
from pymongo import MongoClient

# Create a connection to the MongoDB server
client = MongoClient('mongodb://localhost:27017/')

# Create or access a database
db = client['employees']

# Create or access a collection (similar to a table in SQL)
collection = db['employees']

# Insert data into the collection
employee1 = {'id': 1, 'name': 'John Doe', 'age': 30, 'department': 'Sales'}
employee2 = {'id': 2, 'name': 'Jane Smith', 'age': 35, 'department': 'Marketing'}
employee3 = {'id': 3, 'name': 'Mike Johnson', 'age': 40, 'department': 'IT'}

collection.insert_one(employee1)
collection.insert_one(employee2)
collection.insert_one(employee3)

# Retrieve data from the collection
results = collection.find({'department': 'Sales'})

# Print the retrieved data
for result in results:
    print(result)

# Close the MongoDB connection
client.close()

# Import the required Hadoop libraries
from hdfs import InsecureClient

# Connect to the HDFS cluster
client = InsecureClient('http://localhost:50070')

# Create a new directory in HDFS
client.makedirs('/my_directory')

# Upload a file to HDFS
client.upload('/my_directory', 'local_file.txt')

# List files in a directory in HDFS
files = client.list('/my_directory')
for file in files:
    print(file)

# Download a file from HDFS
client.download('/my_directory/local_file.txt', 'local_destination.txt')

# Delete a file from HDFS
client.delete('/my_directory/local_file.txt')

# Close the connection
client.close()

# Import the required YARN libraries
from yarn_api_client import ApplicationMaster, ApplicationMasterConfig

# Configure the YARN application
config = ApplicationMasterConfig('my_application', memory='2g', vcores=2)

# Create the YARN application master
app_master = ApplicationMaster(config)

# Start the YARN application
app_master.start()

# Monitor the YARN application status
status = app_master.get_status()
print('YARN Application Status:', status)

# Stop the YARN application
app_master.stop()

# Import the required MapReduce libraries
from mrjob.job import MRJob

# Define a MapReduce job
class WordCount(MRJob):
    
    def mapper(self, _, line):
        words = line.split()
        for word in words:
            yield word, 1

    def reducer(self, key, values):
        yield key, sum(values)

# Create an instance of the WordCount job
job = WordCount(args=['input_file.txt'])

# Run the MapReduce job
with job.make_runner() as runner:
    runner.run()

    # Retrieve the output
    for key, value in job.parse_output(runner.cat_output()):
        print(key, value)

from mrjob.job import MRJob

# Define a MapReduce job
class MyMapReduce(MRJob):

    # Mapper function
    def mapper(self, _, line):
        # Split the line into words
        words = line.split()

        # Emit each word as key-value pair
        for word in words:
            yield word, 1

    # Reducer function
    def reducer(self, key, values):
        # Sum up the counts for each word
        total_count = sum(values)

        # Emit the word and its total count
        yield key, total_count

    # Combiner function (optional)
    def combiner(self, key, values):
        # Perform a local aggregation of counts
        total_count = sum(values)

        # Emit the word and its partially aggregated count
        yield key, total_count

    # Configure the job
    def configure_args(self):
        # Enable the sorting step for shuffle and sort
        return ['-D', 'mapreduce.job.output.key.comparator.class=org.apache.hadoop.mapreduce.lib.partition.KeyFieldBasedComparator',
                '-D', 'mapreduce.partition.keycomparator.options=-k1,1']

# Create an instance of the MapReduce job
job = MyMapReduce(args=['input_file.txt'])

# Run the MapReduce job
with job.make_runner() as runner:
    runner.run()

    # Retrieve the output
    for key, value in job.parse_output(runner.cat_output()):
        print(key, value)