Introduction to Databricks
Duration: 2 Hours
Format: Hands-on Workshop
Level: Beginning
Data is everywhere, but keeping your data engineering, data science, and business analytics teams from tripping over each other is the real challenge. Introduction to Databricks is a hands-on course designed to take you from a Databricks novice to a confident navigator of the industry's leading unified data platform.
Built on top of Apache Spark, Databricks blends the best of data warehouses and data lakes into a single "Lakehouse" architecture. In this course, we will demystify the platform, break down the jargon, and get you writing code, building pipelines, and collaborating in real time.
What You'll Learn
Navigate the Workspace: Master the Databricks UI, spin up and manage compute, and organize your assets.
The Power of Notebooks: Write, execute, and share code seamlessly using interactive, multi-language notebooks (we will concentrate on SQL and Python).
Understand the Lakehouse: Learn how Delta Lake brings reliability, ACID transactions, and lightning-fast speed to object storage.
Data Ingestion & Transformation: Write Spark SQL and PySpark commands to read, clean, and transform large-scale datasets.
Orchestration & Jobs: Build and schedule automated data pipelines to transform raw data into production-ready insights.
Course Breakdown
Module 1: Welcome to the Lakehouse
An introduction to the Databricks philosophy. We'll explore the architecture, set up your account, and spin up compute without breaking a sweat.
Module 2: The Collaborative Workspace
Dive deep into Databricks Notebooks. Learn how to collaborate with teammates in real time and switch effortlessly between Python and SQL in the same file.
Module 3: Delta Lake Essentials
Discover why Delta Lake is the backbone of modern data architecture. We will cover data reliability and Time Travel (querying older versions of your data).
Module 4: ETL with Lakeflow Spark Declarative Pipelines
Get hands-on with data processing. You'll extract raw data, transform it using Spark SQL, and load it into clean, accessible gold-level tables.
Module 5: Automation with Lakeflow Jobs
Move from manual code to production. Learn how to create Lakeflow Jobs and monitor job runs
Who This Course Is For
Data Analysts looking to scale up from traditional SQL databases to massive cloud datasets.
Data Engineers wanting to master the industry-standard platform for managed Apache Spark.
Data Scientists eager to streamline their experimentation and collaborate more effectively with engineering teams.
Tech Managers & Architects evaluating Databricks for their organization's data strategy.
Prerequisites: A basic understanding of SQL or Python, alongside general data concepts (like tables and joins), is all you need. No prior experience with Apache Spark or cloud infrastructure is required!