A collection of sample tutorials and examples to demonstrate the capabilities of Databricks Free Edition . These tutorials are designed to help you get started with data engineering, data science, and analytics using Databricks' free tier.

• A Databricks Community Edition account (free): Sign up here
• Basic knowledge of Python and SQL
• Familiarity with data analysis concepts

1. Sign up for Databricks Free Edition
2. Create a new cluster and a new workspace in your Databricks workspace
3. Import the notebooks from this repository into your workspace
4. Follow along with the tutorials step by step

• Basic Data Exploration: Load, explore, and understand datasets
• Data Cleaning: Handle missing values, outliers, and data quality issues
• Statistical Analysis: Descriptive statistics and data profiling

• Interactive Charts: Create visualizations using Databricks' built-in plotting
• Advanced Visualization: Use matplotlib, seaborn, and plotly
• Dashboard Creation: Build simple dashboards for data insights

• SQL Fundamentals: Query data using Spark SQL
• Advanced SQL: Window functions, CTEs, and complex queries
• Data Warehousing: Create tables, views, and optimize queries

• Data Transformation: Clean and transform raw data
• Batch Processing: Process large datasets efficiently
• Data Pipelines: Create simple data workflows

• ML Basics: Introduction to MLlib and scikit-learn
• Classification: Build predictive models
• Clustering: Unsupervised learning examples
• Model Evaluation: Assess model performance

• Sales Analytics: Analyze sales performance and trends
• Customer Segmentation: Group customers based on behavior
• Time Series Analysis: Analyze temporal data patterns
• More on the fly

DatabricksDemo/
├── README.md                 # This file
├── tutorials/               # Tutorial notebooks and scripts
│   ├── 01-data-exploration/
│   ├── 02-sql-basics/
│   ├── 03-sql-analytics/
│   ├── 04-etl-processing/
│   ├── 05-machine-learning/
│   └── 06-real-world-examples/
├── datasets/               # Sample datasets
    ├── sales_data.csv
    ├── customer_data.csv
    ├── time_series_data.csv
    └── . . .

• Data Engineering: How to process and transform data at scale
• Data Science: Build and evaluate machine learning models
• SQL Analytics: Query and analyze data using Spark SQL
• Visualization: Create compelling charts and dashboards

These tutorials showcase what you can accomplish with Databricks Free Edition:

• Compute: Single-node serverless cluster with up to 15GB RAM
• Storage: 5GB of storage for notebooks and data
• Runtime: Latest Databricks Runtime with Spark, Delta Lake, and MLflow
• Languages: Python, Scala, SQL, and R support
• Libraries: Access to popular data science and ML libraries
• Collaboration: Share notebooks and work with others

All tutorials use publicly available datasets that are suitable for learning:

• Sales Data: E-commerce transaction records
• Customer Data: Customer demographics and behavior
• Time Series Data: Stock prices and web traffic metrics
• ML Datasets: Classic datasets for machine learning practice

• Databricks Free Edition Documentation
• Databricks Community Forum
• Apache Spark Documentation