What is Databricks?

TL;DR

Databricks is a cloud-based unified analytics platform built on Apache Spark. It combines your data warehouse and data lake into a single lakehouse — so data engineers, data scientists, and analysts all work on the same data, in the same place, without copying it around.

The Big Picture

Before Databricks, companies had two separate systems: a data lake (cheap storage, messy data) and a data warehouse (expensive, clean, fast queries). Databricks merges them into a lakehouse — you get the low cost of a lake with the reliability and speed of a warehouse.

Databricks Lakehouse: data sources flow into Delta Lake storage, accessed by engineering, science, and BI workloads
Explain Like I'm 12

Imagine your school has two libraries. One is a giant messy warehouse with every book ever — cheap but impossible to find anything. The other is a small, organized library with only the popular books — fast to search but expensive to maintain. Databricks is like combining both into one super-library where every book is organized AND you have access to everything. Plus, it gives you AI-powered librarians (notebooks) to help you find answers in seconds.

Who is Databricks For?

RoleWhat You Do on Databricks
Data EngineerBuild ETL pipelines, manage Delta tables, orchestrate workflows
Data ScientistTrain ML models, run experiments with MLflow, use AutoML
Data AnalystWrite SQL queries, build dashboards, explore data in notebooks
Analytics EngineerTransform data with dbt or SQL, manage data quality

What You'll Learn

🧱
Core Concepts
Lakehouse, Delta Lake, Unity Catalog, clusters, notebooks, workflows — the building blocks.
🔺
Delta Lake Deep Dive
ACID transactions on a data lake, time travel, schema enforcement, and optimization.
⚡
Spark SQL on Databricks
Write SQL at scale — temporary views, CTEs, window functions, and performance tuning.
🧪
MLflow on Databricks
Track experiments, register models, and deploy ML to production with MLflow.
💬
Interview Questions
30+ Databricks interview questions with hidden answers, organized by topic.
Start Learning: Core Concepts →

Key Capabilities

Cloud-native: Databricks runs on AWS, Azure, and GCP. You pick the cloud — Databricks manages the Spark clusters, scaling, and infrastructure.
CapabilityWhat It Means
Delta LakeACID transactions on your data lake — no more corrupted files or partial writes
Unity CatalogOne place to govern access, lineage, and discovery across all your data
WorkflowsSchedule and orchestrate notebooks, Python scripts, or dbt jobs
Databricks SQLServerless SQL warehouse for BI queries — connect Tableau, Power BI, etc.
MLflowTrack experiments, register models, deploy to production
AutoMLAutomatically train and compare ML models with a few clicks
NotebooksInteractive coding environment supporting Python, SQL, Scala, and R

Databricks vs. Alternatives

FeatureDatabricksSnowflakeBigQuery
ArchitectureLakehouse (open Delta Lake)Cloud data warehouseServerless warehouse
Open formatsDelta, Parquet, IcebergProprietary + IcebergProprietary
ML / Data ScienceBuilt-in (MLflow, AutoML)Snowpark MLVertex AI integration
Real-time streamingStructured StreamingSnowpipe StreamingPub/Sub + Dataflow
SQL analyticsDatabricks SQL (serverless)Native SQLNative SQL
Pricing modelDBU-based (per compute)Credit-based (per compute)Per-query + storage

Test Yourself

Q: What problem does the "lakehouse" architecture solve?

It eliminates the need for separate data lake and data warehouse systems. You get the cheap, scalable storage of a lake with the reliability, ACID transactions, and query performance of a warehouse — all in one system.

Q: What is Delta Lake, and why does it matter?

Delta Lake is an open-source storage layer that adds ACID transactions, schema enforcement, and time travel to data lakes. It matters because raw data lakes are unreliable — partial writes, schema drift, and no rollback. Delta Lake fixes all of that.

Q: Name three roles that use Databricks and what each does.

Data Engineers build ETL pipelines and manage Delta tables. Data Scientists train ML models and track experiments with MLflow. Data Analysts run SQL queries and build dashboards using Databricks SQL.

Q: How is Databricks different from Snowflake?

Databricks is a lakehouse (open Delta Lake format, built-in ML with MLflow, Spark-based). Snowflake is a cloud data warehouse (proprietary storage, SQL-focused, Snowpark for ML). Databricks is stronger for data engineering and ML; Snowflake is stronger for pure SQL analytics.

Q: What is Unity Catalog?

Unity Catalog is Databricks' unified governance layer. It provides centralized access control, data lineage tracking, and data discovery across all workspaces, catalogs, schemas, and tables — one place to manage who can access what.