Databricks Example


                Databricks Example

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.


# 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


  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.


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))

3. Exploratory Analysis and Visualization

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


import matplotlib.pyplot as plt


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


4. Machine Learning (ML)

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


from import VectorAssembler

from 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 =


# 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 (

Databricks Training Demo Day 1 Video:

You can find more information about Databricks Training in this Dtabricks Docs Link



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