01. What is Databricks?

Databricks is a cloud data and AI platform built around the lakehouse architecture. It combines data engineering, SQL analytics, streaming, machine learning, and AI workloads on a common governed data foundation.

A useful mental model is:

Cloud Object Storage
        │
     Delta Lake
        │
 ┌──────┼──────────┐
 │      │          │
Spark  SQL       ML / AI
 │      │          │
 └──────┼──────────┘
        │
  Unity Catalog
        │
 Governance + Lineage

Databricks is built on Apache Spark, while Delta Lake provides the table/storage layer and Unity Catalog provides unified governance for data and AI assets.

Databricks is more than Spark

Technology	Role
Apache Spark	Distributed processing
Delta Lake	Reliable lakehouse tables
Unity Catalog	Governance, permissions, lineage
Databricks SQL	SQL analytics
Workflows / Jobs	Orchestration
Auto Loader	Incremental file ingestion
Lakeflow	Data engineering and pipeline capabilities
MLflow / ML capabilities	Machine learning lifecycle
Mosaic AI	AI and GenAI workloads

Why organizations use Databricks

A traditional enterprise may have separate platforms for ETL, data lakes, warehouses, streaming, data science, ML, AI, and governance. A lakehouse attempts to bring these workloads onto a common data foundation.

Enterprise example

SAP ─────────┐
Salesforce ──┤
RDBMS ───────┤
APIs ────────┤──► Ingestion ─► Databricks Lakehouse
Kafka ───────┘                         │
                                      ├── Bronze
                                      ├── Silver
                                      └── Gold
                                           │
                         ┌─────────────────┼────────────────┐
                         ▼                 ▼                ▼
                       BI/SQL             ML              GenAI

Key takeaway

Databricks should be understood as a data + analytics + AI platform, not merely as a hosted Spark cluster.

Next

Continue with the next topic in 01. Databricks Foundations.