Lakehouse vs. warehouse for big data

1. Lakehouse vs. warehouse for big data

Lakehouses and warehouses have only come up in passing so far. Let's examine both in more detail.

2. Why Fabric has different data stores

Fabric offers two places to store data, a lakehouse and a warehouse, because different kinds of work need different kinds of storage. Think of a manufacturing company with two storage areas. One is a flexible workshop holding raw materials, unfinished parts, and completed components. The other is a highly organized stockroom holding finished products in labeled locations. Fabric splits its storage the same way: lakehouses hold raw and structured data side by side, warehouses hold structured data only.

3. What is a lakehouse?

A lakehouse is the flexible workshop. Just as a workshop holds raw materials alongside finished components, a lakehouse holds raw files, such as CSV, JSON, or Parquet, alongside structured tables. That flexibility makes it the usual home for data engineering and data science work.

4. What is a warehouse?

A warehouse is more like a carefully managed stockroom. Everything in it is structured data, organized into tables. A finance team might keep revenue, costs, customers, and accounts here. Because those tables follow a relational structure, a warehouse is built for SQL-first analytics. That makes it familiar territory for analysts and database users.

5. Common lakehouse scenarios

We reach for a lakehouse whenever data arrives as files and we want to keep the original. A workshop keeps its crates of raw material even after building components from them. A lakehouse does the same: engineers transform the files into tables, and the originals stay available alongside them.

6. Common warehouse scenarios

We reach for a warehouse when the data is already curated and we need to report on it regularly. Daily updates on business activity arrive in a consistent shape, the way finished goods arrive in a stockroom already inspected and labeled. Because every table follows a known structure, the reports built on them stay predictable.

7. The role of SQL for downstream analytics

A workshop and a stockroom both need a way to find what's inside them. Data stores are no different. Everything that happens to data after it's stored, from querying to modeling to reporting, is what we call downstream analytics. Lakehouses and warehouses both hold large volumes of diverse data, but any single piece of analysis needs only a small slice of it. SQL, short for Structured Query Language, was built for exactly that. It lets us define which data to pull from a table. Later in this chapter, you'll write SQL against a Fabric lakehouse.

8. How downstream analytics works

Downstream analytics happens in three layers. First, the data preparation layer, where ingested data is organized into curated tables that are easy to query. A warehouse is usually already organized this way. Next, the modeling layer. A semantic model is the business-friendly layer that reports read from; it pulls from those curated tables and translates them into business metrics and relationships. Finally, the reporting layer. The data and the model come together in a report that end users actually read.

9. Lakehouse vs. warehouse

Let's recap the differences. A lakehouse stores both raw files and tables. A warehouse only stores tables. Because a lakehouse is designed for raw data, analytics on it usually needs a curated table prepared first. A warehouse already stores data in an analytics-friendly format. Both are queried with SQL, but not in the same way. A lakehouse’s SQL analytics endpoint is read-only, so you can SELECT from its tables but not change them there. A warehouse supports full T-SQL, Microsoft’s SQL dialect, so you can also INSERT and UPDATE rows.

10. Let's practice!

Now let's practice choosing between the flexible workshop and the structured stockroom.

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