Introduction to Databricks

Databricks is a unified analytics platform built on top of Apache Spark. It pioneered the “Lakehouse” architecture, which provides the performance of a data warehouse with the massive scale and flexibility of a data lake.

🔗 Learning Resources

• Official Academy: Databricks Lakehouse Fundamentals - Free accreditation for students.
• Community Edition: Databricks Community Cloud - Access a free micro-cluster for hands-on practice.
• Delta Lake Docs: Delta Lake Guide - Understand how to bring ACID transactions to big data.

🧱 Databricks Practice Tasks: The Lakehouse Challenge

Databricks is a unified data analytics platform. It combines the best of Data Warehouses (SQL/Performance) and Data Lakes (Storage/Flexibility) into a Lakehouse.

📝 Practice Tasks: Notebooks & Delta Lake

Task 1: Cluster & Notebook Setup

Goal: Understand the Compute layer.

• Requirement: Spin up a “Personal Compute” or “Community Edition” cluster.
• Challenge: Create a New Notebook.
  • Set the default language to Python.
  • Use a “Magic Command” (%sql) in the second cell to switch to SQL.
• Verification: Run spark.version in Python and SELECT 1 in SQL.

Task 2: The Magic of Delta Lake (Time Travel)

Goal: Understand ACID transactions on a Data Lake.

• Requirement: Load the grocery CSV into a Delta Table.

  df.write.format("delta").saveAsTable("sales_delta")
  

• Challenge: 1. Update a price in the table: UPDATE sales_delta SET price = 10.0 WHERE id = 1. 2. Use Time Travel to see the previous version:

  SELECT * FROM sales_delta VERSION AS OF 0
  

• Discussion: Why is “Time Travel” a lifesaver for Data Engineers during a pipeline failure?

Task 3: Multi-Language Transformation (Polyglot)

Goal: Use the best tool for the job within one notebook.

• Requirement: 1. Use PySpark to read the raw data and filter out nulls. 2. Register the result as a Temporary View: df.createOrReplaceTempView("cleaned_data"). 3. Use SQL (%sql) to perform a GROUP BY on that temporary view.
• Challenge: Why is this “Polyglot” approach better than forcing everything into one language?

Task 4: Databricks Utilities (dbutils)

Goal: Interact with the file system (DBFS).

• Requirement: Use dbutils.fs.ls("/") to list the root directories.
• Challenge: Create a new folder named /tmp/my_project/ and move a sample file into it using dbutils.fs.mv.
• Command: display(dbutils.fs.ls("/tmp/my_project/"))

🏗️ Mini Project: The Medallion Architecture

Build a simplified “Medallion” pipeline in a single notebook:

1. Bronze Layer (Raw): Load the raw CSV exactly as it is into a Delta table named bronze_sales.
2. Silver Layer (Cleaned): Filter out negative prices, fix date formats, and deduplicate. Save as silver_sales.
3. Gold Layer (Aggregated): Create a final table gold_category_revenue that sums up revenue for the business dashboard.
4. Optimization: Run OPTIMIZE gold_category_revenue ZORDER BY (sale_date) to speed up queries.