Introduction
Microsoft Fabric provides multiple analytical data stores, each designed for different data patterns and workloads. Choosing the right store for your scenario is one of the most important decisions you make when building an analytics solution. The right choice affects how you ingest data, how your team queries it, and how well your solution adapts as requirements evolve.
The three primary analytical data stores in Fabric are the lakehouse, the warehouse, and the eventhouse. Each one stores data in OneLake using an open format, but they differ in query language support, write capabilities, and the types of workloads they handle best.
Suppose you're a data professional at a retail organization that's adopting Microsoft Fabric. Your team needs to build analytics solutions for several business groups. The sales team wants structured reporting with complex joins. The data science team needs to explore a mix of transaction data and web clickstream logs. The operations team monitors IoT sensor data from distribution centers in real time. Each group has different data types, query preferences, and performance requirements.
Your task is to evaluate Fabric's analytical data stores and recommend the right one for each business group. To do that, you need to understand what each store does well, when to use it, and when a different option is a better fit.
In this module, you:
- Survey the three analytical data store options in Microsoft Fabric: lakehouse, warehouse, and eventhouse.
- Evaluate the strengths and ideal use cases for each data store.
- Apply a decision framework to match data characteristics and team skills to the right store.
- Practice choosing the appropriate data store for real-world business scenarios.
By the end of this module, you're able to evaluate lakehouse, warehouse, and eventhouse capabilities and confidently choose the appropriate data store for a given business scenario.