Tutorial 1: Build Your First Lakehouse

Build a complete data lakehouse using Databricks and the medallion architecture. You'll ingest raw e-commerce data, clean and transform it, and create business metrics ready for analytics.

Time: 2-3 hours Level: Beginner Prerequisites: Basic SQL and Python, Databricks account

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What You'll Build

A production-ready data pipeline that:

• Ingests raw e-commerce data (orders, customers, products)
• Implements the medallion architecture (Bronze → Silver → Gold)
• Validates data quality with automated checks
• Creates business metrics for analytics
• Runs on a scheduled basis

Architecture:

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Learning Objectives

By the end of this tutorial, you'll be able to:

• ✅ Create and manage Delta Lake tables
• ✅ Implement medallion architecture
• ✅ Write data quality checks
• ✅ Use SQL and PySpark interchangeably
• ✅ Schedule and monitor data pipelines
• ✅ Optimize Delta tables for performance

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Part 1: Setup

Step 1.1: Create a Cluster

1. Navigate to Compute in sidebar
2. Click Create Cluster
3. Configure:
4. Click Create Cluster and wait ~5 minutes

Step 1.2: Create a Notebook

1. Go to Workspace → Users → Your email
2. Click dropdown → Create → Notebook
3. Configure:

Step 1.3: Create Database

Run this in your notebook:

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Part 2: Bronze Layer - Raw Data Ingestion

The Bronze layer stores raw data exactly as received from source systems.

Step 2.1: Generate Sample Data

Step 2.2: Write to Bronze Delta Table

Step 2.3: Verify Bronze Table

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Part 3: Silver Layer - Cleaned and Validated Data

The Silver layer contains cleaned, validated, and enriched data.

Step 3.1: Clean and Validate Data

Step 3.2: Implement Data Quality Checks

Step 3.3: Write to Silver Delta Table

Step 3.4: Add Table Constraints

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Part 4: Gold Layer - Business Metrics

The Gold layer contains aggregated business metrics ready for consumption.

Step 4.1: Create Daily Sales Metrics

Step 4.2: Create Product Performance Metrics

Step 4.3: Create Customer Segmentation

Step 4.4: Create Regional Performance

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Part 5: Optimization

Step 5.1: Optimize Delta Tables

Step 5.2: Table Statistics

Step 5.3: Enable Auto-Optimize

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Part 6: Create Analytics Views

Step 6.1: Create Business-Friendly Views

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Part 7: Time Travel and Data Recovery

Delta Lake provides time travel capabilities for data recovery and auditing.

Step 7.1: View Table History

Step 7.2: Query Historical Data

Step 7.3: Restore Previous Version

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Part 8: Create Automated Pipeline

Step 8.1: Create Orchestration Notebook

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Part 9: Monitoring and Validation

Step 9.1: Create Monitoring Dashboard

Step 9.2: Validate Data Quality

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Part 10: Cleanup and Next Steps

Step 10.1: Review What You Built

Step 10.2: Save Your Work

Save the notebook:

1. File → Save
2. File → Export → DBC Archive (backup)

Step 10.3: Schedule as Job (Optional)

To run this pipeline on a schedule:

1. Go to Workflows → Create Job
2. Configure:
   • Name: ecommerce_lakehouse_pipeline
   • Task: Select your notebook
   • Cluster: Create new job cluster (cost-effective)
   • Schedule: Daily at 6 AM
3. Click Create

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Summary

Congratulations! You've built a complete data lakehouse with:

✅ Bronze Layer - Raw data ingestion ✅ Silver Layer - Cleaned and validated data ✅ Gold Layer - Business metrics and aggregations ✅ Data Quality - Automated validation checks ✅ Optimization - ZORDER and table statistics ✅ Analytics - Business-friendly views ✅ Time Travel - Historical data queries ✅ Monitoring - Pipeline validation

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Key Concepts Learned

Medallion Architecture

• Bronze: Raw data, minimal transformation
• Silver: Cleaned, validated, enriched
• Gold: Business-level aggregations

Delta Lake Features

• ACID transactions
• Time travel
• Schema evolution
• OPTIMIZE and ZORDER

Best Practices

• Data quality checks at each layer
• Proper partitioning and optimization
• Clear separation of concerns
• Automated validation

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Next Steps

Immediate

1. Experiment with different aggregations in Gold layer
2. Add more data quality checks
3. Create visualizations in Databricks SQL

Intermediate

4. Implement incremental processing
5. Add streaming ingestion
6. Integrate with BI tools (Tableau, Power BI)

Advanced

7. Complete Tutorial 2: Real-Time Streaming
8. Implement Delta Live Tables
9. Add Unity Catalog governance
10. Review Best Practices

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Troubleshooting

Issue: Table not found

Issue: Out of memory

Issue: Slow queries

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Congratulations on completing Tutorial 1! 🎉

You've built a solid foundation in Databricks and Delta Lake. Ready for more?

[Next: Tutorial 2 (Coming Soon)] | Back to Tutorials | View Best Practices