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What is Databricks?

The Unified Data Platform, Explained

A practical introduction for data professionals coming from the Microsoft ecosystem

Part 1 of 5: Databricks Fundamentals
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The Data Problem

Why organizations ended up with two systems for one job

Data Warehouses

Strengths

  • Structured, schema-enforced queries
  • Optimized for BI and reporting
  • Strong SQL support and governance

Limitations

  • Rigid schemas; slow to adapt
  • Expensive to scale storage
  • Poor support for unstructured data

Data Lakes

Strengths

  • Stores any data type at any scale
  • Low-cost object storage
  • Flexible for ML and data science

Limitations

  • No schema enforcement ("data swamp" risk)
  • No ACID transactions
  • Difficult to query reliably for BI
The two-system problem: Organizations ended up running both a data warehouse (for BI teams) and a data lake (for data scientists and engineers). Same data, stored twice, governed differently, with constant sync headaches between the two. This is the problem the Lakehouse was designed to solve.

Data Silos

The same data copied across warehouse, lake, and staging areas. Every copy drifts over time.

Governance Gaps

Different access controls in the warehouse vs. the lake. Who has the "right" version?

Cost Explosion

Paying for storage twice, compute twice, and ETL pipelines to keep everything in sync.

Data Warehouses: structured and reliable, but rigid and expensive.
Data Lakes: flexible and cheap, but no governance or ACID transactions.
This is the problem statement. Most enterprises ended up running both, creating complexity.
Data Silos: same data copied everywhere, every copy drifts.
Governance Gaps: different access controls, no single source of truth.
Cost Explosion: paying for everything twice plus sync pipelines.

The Lakehouse

Lake flexibility with warehouse reliability

Data Lake
Any File Format
Schema on Read
Cost Effective
Weak Governance
Data Warehouse
Structured Tables
Schema on Write
Fast Queries
Rigid & Costly
Data Lakehouse
Any Format
ACID
Fast Queries
Low Cost

Traditional Lake

Partial Writes Possible
Read Consistency Unstable
Rollback None
Versioning Manual
Pre-Delta
vs

Delta Lake

Partial Writes Prevented
Read Consistency Guaranteed
Rollback Built-in
Versioning Built-in
ACID Compliant
A
Atomicity
All or nothing: transactions complete fully or not at all
C
Consistency
Data always moves from one valid state to another
I
Isolation
Concurrent operations don't interfere with each other
D
Durability
Once committed, data persists even through failures
Go deeper: Databricks popularized the Lakehouse pattern, built on top of Delta Lake format (open-source, columnar, ACID-compliant). For a look at how data flows through Bronze, Silver, and Gold layers in a Lakehouse, see our Medallion Architecture guide.
Data Lakes: flexible and cheap, but no governance. This is one half of the old paradigm.
Data Warehouses: structured and reliable, but rigid and expensive. The other half.
The Lakehouse combines both: any format, ACID transactions, fast queries, low cost. Best of both worlds.
Traditional data lakes had real problems: partial writes, inconsistent reads, no rollback.
Delta Lake solves all of these with ACID transactions. This is what makes the Lakehouse possible.
Atomicity: writes complete fully or not at all.
Consistency: data always moves from one valid state to another.
Isolation: concurrent reads and writes don't interfere.
Durability: once committed, data survives failures.
Links to our existing deep-dive guides on Delta and Medallion Architecture.

What Databricks Actually Is

A platform layer, not a cloud replacement

Workspace
Notebooks
SQL Editor
Jobs
Repos
Dashboards
Compute
Clusters
SQL Warehouses
Serverless
Storage & Governance
Delta Lake
Unity Catalog
Volumes
Your Cloud Provider
Azure
AWS
GCP

Databricks orchestrates all four layers. Your data never leaves your cloud.

Key insight: Databricks is a platform layer that runs on your cloud provider. It does not replace Azure, AWS, or GCP. It orchestrates compute and governs data within the cloud you already use. Your data stays in your storage account.

What This Means in Practice

Your Data, Your Storage

Data lives in your own ADLS Gen2 (Azure), S3 (AWS), or GCS (Google) storage. Databricks reads and writes to it, but never takes ownership of it.

Compute Scales Independently

Spin up clusters when you need processing power, shut them down when you don't. Storage and compute are fully decoupled.

Two Bills, One Platform

You pay your cloud provider for infrastructure (storage, networking) and Databricks for the platform layer (workspace, runtime optimization, governance).

Azure Databricks
Azure Databricks is the Azure-native deployment of the Databricks platform. Same core product, integrated with Azure Active Directory, deployed into your Azure subscription, and billed through Azure. If you already work in the Microsoft ecosystem, this is typically where you start.
Workspace layer: notebooks, SQL editor, jobs, repos, dashboards.
Compute layer: clusters, SQL warehouses, serverless options.
Storage and governance layer: Delta Lake, Unity Catalog, Volumes.
Cloud provider layer: Azure, AWS, GCP. Databricks runs on top of these.
Key architectural point: Databricks is a layer ON your cloud, not a replacement for it.
Three practical implications: data stays in your storage, compute is decoupled, two bills.
Azure Databricks is the Azure-native deployment. Most relevant for the Microsoft ecosystem audience.

Four Service Areas

What you can actually do on the Databricks platform

Databricks organizes its capabilities into four pillars. Most organizations start with one or two, then expand as their data maturity grows.

Data Engineering
Languages Python, SQL, Scala
Key Tool Delta Live Tables
Also Workflows, Auto Loader
Output Governed Delta tables
SQL Analytics
Languages SQL
Key Tool SQL Warehouses
Also Editor, Dashboards
Output BI query endpoints
Machine Learning
Languages Python, R, Scala
Key Tool MLflow
Also Feature Store, AutoML
Output Models + endpoints
Streaming
Languages Python, SQL, Scala
Key Tool Structured Streaming
Also DLT streaming mode
Output Live Delta tables
Four pillars. Most organizations start with one or two and expand over time.
Data Engineering: the bread and butter. Delta Live Tables is the flagship tool here.
SQL Analytics: the entry point for SQL-first users. Serverless SQL Warehouses connect to Power BI.
Machine Learning: MLflow is open source, which is a differentiator vs. proprietary ML platforms.
Streaming: same Delta Live Tables framework, just in streaming mode. Unified batch + streaming.

How It Works With Cloud Providers

Databricks runs on your cloud, not instead of it

Databricks Databricks
Azure
AWS
GCP
Area Databricks Provides Your Cloud Provides
Development Workspace + notebooks Object storage
Compute Optimized Spark + Photon Virtual machines
Governance Unity Catalog Identity provider
Pipelines Delta Live Tables Encryption + keys
Cost Platform fee (DBUs) Infrastructure billing
Databricks sits on top of all three major cloud providers. Same platform, different infrastructure underneath.
Clear division of responsibility: Databricks provides the data platform; the cloud provides the infrastructure.

Better Together

Both/And, not Either/Or

Databricks and Microsoft Fabric are complementary platforms. Many organizations use both: Databricks for heavy engineering and ML, Fabric and Power BI for analytics and reporting. The real question is not "which one?" but "where does each fit?"

Where Databricks Excels

Large-scale ETL and data engineering
ML model training and serving
Multi-cloud data governance
Real-time streaming pipelines
Open-source toolchain (Spark, MLflow, Delta)

Where Microsoft Fabric Excels

Power BI semantic models and reporting
OneLake unified storage with shortcuts
T-SQL analytics (familiar for SQL Server teams)
Low-code data prep (Dataflow Gen2)
Microsoft 365 and Teams integration
Databricks

Databricks

Processing + ML

OneLake Shortcuts
DirectQuery to SQL Warehouse
Unity Catalog Mirroring
Fabric

Fabric

OneLake + Analytics

Power BI

Reporting + Dashboards

Explore the Microsoft side: Our Direct Lake guide covers how Fabric connects to data (Import, DirectQuery, and Direct Lake modes). The Fabric Engines & Items guide explains OneLake shortcuts and how Fabric's own compute engines compare. In practice, many organizations use Databricks for heavy ETL and ML workloads, then surface results through Fabric and Power BI for business consumption.
Core message: these platforms are complementary, not competing. Most mature organizations use both.
Where Databricks excels: heavy ETL, ML, multi-cloud governance, streaming, open-source.
Where Fabric excels: Power BI, OneLake, T-SQL, low-code, Microsoft 365 integration.
Three concrete integration mechanisms. These are real, shipping features, not marketing promises.
Links to our Fabric guides for the audience that wants to go deeper on the Microsoft side.

Who Uses Databricks

Four personas, one platform

Different roles interact with different parts of the platform. Here are the four primary personas you will encounter in a Databricks environment.

Data Engineer

Builds and orchestrates pipelines using notebooks, Delta Live Tables, and Workflows. Python and SQL are the primary languages.

SQL Analyst

Queries data through SQL Warehouses and the SQL Editor. Builds dashboards and connects BI tools. No Python required.

Data Scientist

Trains and deploys ML models using MLflow, Feature Store, and notebook experiments. Leverages GPU clusters for deep learning.

Platform Admin

Manages Unity Catalog, workspace access, cluster policies, and cost controls. The person who keeps the platform secure and efficient.

You do not need to be a Python developer

This is one of the most common misconceptions about Databricks. SQL-first users can work entirely within SQL Warehouses and the SQL Editor. If you are comfortable writing T-SQL in SQL Server or Fabric, you already have the foundation to query data in Databricks.

Four primary personas in a Databricks environment. Most people fall into one of these.
Data Engineer: builds pipelines with notebooks, Delta Live Tables, Workflows. Python and SQL.
SQL Analyst: queries via SQL Warehouses, builds dashboards, connects BI tools. No Python needed.
Data Scientist: trains ML models with MLflow, Feature Store, AutoML. Uses GPU clusters.
Platform Admin: manages Unity Catalog, access, cluster policies, cost controls. The security person.
Key takeaway for the Power BI / Fabric audience: you do NOT need to know Python to use Databricks.

Example Data Flow

A common Databricks architecture from source systems to business consumption

Source

Data source systems

ERP
CRM
Files

Data Engineering

Ingest, transform, and store with Databricks

Ingest
Layer
Data Factory
Orchestration
Auto Loader
Streaming Ingest
Transform
Layer
Delta Live Tables
Managed Pipelines
Notebooks
Custom Transforms
Store
Layer
Bronze
Delta Lake
Silver
Delta Lake
Gold
Delta Lake

Serving Layer

Query the data

Databricks
SQL
Unity
Catalog

Analytics and Reporting

Business and data science consumption

Data
Science
MLflow
Notebook
Notebook
Insights
Databricks AI
Reporting
Power BI
DirectQuery
Dashboards
SQL Dashboards

Unity Catalog: Governance and Lifecycle

Unified data governance across all layers

Discover
Lineage
Access Control
Audit
Quality
Compliance
Sources: same as any data platform. ERP, CRM, and flat files feed into the ingestion layer.
Data Engineering: Azure Data Factory orchestrates, Auto Loader handles streaming file ingest. Delta Live Tables for managed ETL, Notebooks for custom transforms. Bronze/Silver/Gold all stored as Delta Lake tables.
Serving Layer: Databricks SQL warehouses are the primary query endpoint. Unity Catalog provides the namespace and access layer. Power BI connects via DirectQuery to Databricks SQL.
Analytics and Reporting: MLflow for ML lifecycle, notebooks for experimentation. Databricks AI (Genie) for natural language queries. Power BI via DirectQuery and native SQL Dashboards for reporting.
Unity Catalog spans the entire architecture: discovery, lineage tracking, access control, audit logging, data quality monitoring, and compliance.

Ready to explore how Databricks fits into your data platform?

Whether you are evaluating Databricks alongside Microsoft Fabric or planning a hybrid architecture, we can help you design the right approach for your organization.

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