Most big data platforms today don't run in a company's own server room. They run in the cloud, where storage and computing power can be rented by the hour and scaled up or down as needed. This module looks at the three big providers, AWS, Microsoft Azure, and Google Cloud, and how they differ.
You'll also meet the data lakehouse, an architecture that combines the cheap, flexible storage of a data lake with the reliability and fast queries of a data warehouse, so one copy of the data can serve reports, pipelines, and machine learning. To see it in practice, you'll finish with Azure Databricks, a popular platform built around Spark and the lakehouse idea.
This module brings together storage, processing, and modelling from the earlier ones. Once you've finished it, move on to Governance, Security & Analytics.
How the three major cloud providers compare on market share, services, downtime, and pricing.
How data storage evolved from warehouses to data lakes to lakehouses, and the challenges and key features of the lakehouse approach.
How a lakehouse differs from a warehouse and a lake, and how open table formats, shared catalogs, and governance tools bring them together.
What Azure Databricks is, why it is used and how it works, with a hands-on walkthrough connecting it to Azure Blob Storage.