This page gives you a quick, practical overview of Databricks—a lakehouse platform that unifies data engineering, analytics, and machine learning.
You’ll see where it fits, when to use it, and how the core pieces work together.
In short, the platform sits on Apache Spark, layers governance and storage reliability on top of your cloud,
and streamlines collaboration with notebooks and jobs. Moreover, the lakehouse pattern combines the
flexibility of data lakes with warehouse-style performance and governance. Because of that, teams can ingest,
transform, and serve data to BI or ML with far less glue code.
Lakehouse = lake flexibility + warehouse reliability, governed end to end.
If you plan to build analytics or ML pipelines, start small: prototype in a notebook, add tested jobs,
and apply centralized governance. For an end-to-end walkthrough, see our guide on building a Delta Lake pipeline.
In addition, the official docs cover deployment, performance, and security controls in depth.
Unity Catalog centralizes permissions, lineage, and audits across workspaces and data products.
Scroll to the flashcards for a rapid refresher. Then, use the links at the end to explore examples and templates.
🔥 Databricks Flashcards
🔥 What is it?
A cloud-native analytics and ML platform built on Apache Spark with a lakehouse design.
💡 What is the Lakehouse?
A pattern that merges data-lake flexibility with warehouse performance and governance.
🧪 What are Notebooks?
Collaborative spaces to run Python, SQL, Scala, or R for ETL, analytics, and ML.
🛠️ What is a Cluster?
A set of compute resources that executes notebooks, jobs, and SQL queries.
📦 What is Delta Lake?
An open-source storage layer adding ACID transactions and schema enforcement to lakes.
⚙️ What is a Job?
A scheduled run of notebooks or code artifacts used to automate pipelines.
Centralized governance for permissions, lineage, discovery, and auditing across assets.
📈 How does MLflow help?
It tracks experiments, manages models, and supports a registry for promotion and deployment.
🌐 Is SQL supported?
Yes—interactive queries, dashboards, and alerts with a familiar SQL interface.
📂 What are Workspaces?
Shared environments where teams store notebooks, libraries, and data assets.
⚡ Is it multi-cloud?
Yes—consistent features on AWS, Microsoft Azure, and Google Cloud.
To begin, ingest a small source, validate with a notebook, and schedule a job. Next, enforce permissions in Unity
Catalog before sharing dashboards. Finally, track models with MLflow and promote versions through a registry.
