Databricks Jobs and Workflows Guide

Introduction

Jobs and workflows allow automation of notebooks and data pipelines.

Step 1: Create Job

Navigate to Workflows → Create Job.

Step 2: Add Tasks

Tasks can include notebooks, scripts, or SQL queries.

Step 3: Schedule Jobs

Configure schedule to run jobs daily or hourly.

Conclusion

Workflows ensure reliable execution of ETL pipelines.

Databricks Lakehouse Architecture Explained (Simple Guide)

Databricks Lakehouse Architecture Explained

The Lakehouse architecture introduced by Databricks is a modern approach that combines the low-cost flexibility of data lakes with the reliability and performance of data warehouses. It provides a single unified platform for analytics, BI, and machine learning.

Why Lakehouse Was Created

Traditional data lakes lacked reliability, while data warehouses were expensive and rigid. Lakehouse solves both problems by offering:

• Low-cost storage
• High-performance queries
• ACID transactions
• Unified governance

The Medallion Architecture (Bronze, Silver, Gold)

1. Bronze Layer – Raw Data

Stores unprocessed data as ingested from source systems.

2. Silver Layer – Clean & Refined Data

Data is cleaned, structured, and validated.

3. Gold Layer – Business-Ready Data

Used for dashboards, analytics, and ML models.

Benefits of the Lakehouse

• Seamless batch and real-time processing
• Faster ETL performance
• Simplified architecture with fewer tools
• Better governance and quality control

Use Cases

• Finance analytics
• Marketing dashboards
• Inventory forecasting
• ML model feature stores

Conclusion

The Databricks Lakehouse is transforming how companies store and process data. Its combination of performance, cost efficiency, and reliability makes it the ideal architecture for modern data-driven organizations.