Here’s a combination of examples to illustrate different Databricks functionalities, along with explanations:

1. Mounting Data Storage

• Scenario: You want to access data from cloud storage like AWS S3 or Azure Blob Storage.

Python

# Replace with your storage account name and access essential

spark. conf.set(

  “fs.s3a.access.key”, “YOUR_AWS_ACCESS_KEY_ID”

)

Spark. conf.set(

  “fs.s3a.secret.key”, “YOUR_AWS_SECRET_ACCESS_KEY”

)

# Mount S3 bucket

duties.fs.mount(

  source = “s3a://your-bucket-name/”,

  mount_point = “/mnt/your-bucket-name/”

)

2. Reading and Transforming Data

• Scenario: You have a CSV file with customer data in your mounted storage.

Python

df = spark. read.option(“header”, True).option(“inferSchema”, True).csv(“/mnt/your-bucket-name/customer_data.csv”)

# Data transformation example

df = df.with column(“signup_month”, df.signup_date.substring(6,2)) 

df.show(5)

3. Exploratory Analysis and Visualization

• Scenario: You want to visualize the distribution of customer signups by month.

Python

import matplotlib.pyplot as plt

signup_counts = df.groupBy(“signup_month”).count().toPandas()

plt.bar(signup_counts[‘signup_month’], signup_counts[‘count’])

plt.xlabel(‘Signup Month’)

plt.ylabel(‘Customer Count’)

plt.title(‘Customer Signups by Month’)

# Display the plot directly in the Databricks notebook

display(plt.gcf()) 

4. Machine Learning (ML)

• Scenario:  Predict customer churn using a simple classification model.

Python

from pyspark.ml.feature import VectorAssembler

from pyspark.ml.classification import LogisticRegression

# Assemble features into a single vector column

assembler = VectorAssembler(inputCols=[“total_purchases”, “avg_spend”], outputCol=”features”)

df = assembler.transform(df)

# Split into training and testing sets

train, test = df.randomSplit([0.7, 0.3], seed=42)

# Train a logistic regression model

LR = LogisticRegression(labelCol=”churn”)

model = lr.fit(train)

# Evaluation

predictions = model.transform(test)

predictions. select(“churn,” “prediction,” “probability”).show()

Important Notes:

• These examples assume you have a Databricks environment and the pyspark library (spark represents your SparkSession).
• Replace placeholders with your specific credentials and file paths.
• You can use different cloud storage providers by adjusting filesystem configurations.
• Databricks supports SQL, Scala, and R for data manipulation and analysis.
• Explore the vast array of ML libraries in Databricks (https://docs.databricks.com/spark/latest/mllib/index.html)