标签：#fabric

title: Why I left Warehouse out of our Fabric deployment scope published: true tags: microsoftfabric, datawarehouse, cicd, devops Our Fabric deployment pipeline handles sixteen item types. Warehouse is not one of them, and that was deliberate. DEFAULT_ITEM_TYPES = [ " DataPipeline " , " Lakehouse " , " Notebook " , " SemanticModel " , # "Warehouse" is intentionally excluded. Warehouse schema deployment must # be handled separately to avoid schema reset risk during publish. " Environment " , " Eventhouse " , ... ] The reason Publishing a warehouse through this path can reset its schema. Not "might behave unexpectedly". The failure mode is that a deployment intended to be additive removes structure, and the thing that removes it is the same routine that successfully deploys the other sixteen types. The choice that follows Two options once you know that. Include it and hope nobody deploys a warehouse without reading the docs. The pipeline supports everything, and one day someone promotes a change on a Friday and finds out. Or exclude it, document why, and handle warehouse deployment as its own problem with its own tooling. I took the second. An automation that covers most cases and silently corrupts the rest is worse than one that covers most cases and refuses the rest. The refusal is visible. The corruption is not. Making the exclusion loud An exclusion is only useful if someone notices it. Three things help: The comment sits inside the list , not in a doc nobody opens. Anyone reading the item types sees the gap and the reason in the same glance. It is in the README under known limitations, next to the other things the framework does not do. There is a test. It asserts Warehouse is absent from the default scope: def test_warehouse_stays_excluded ( self ): """ Warehouse publish can reset schema, so it is handled separately. """ self . assertNotIn ( " Warehouse " , deploy . DEFAULT_ITEM_TYPES ) That test looks silly. It is asserting that a string is missing from a list.

T-SQL on Microsoft Fabric - Episode 1: Mastering Data Retrieval with SELECT, WHERE, and ORDER BY Learning Goals In this lesson, you will learn how to: Read data from tables using SELECT Filter rows with WHERE Sort query results with ORDER BY Get familiar with standard T-SQL syntax Practice directly in Microsoft Fabric Warehouse 1. Understanding Database and Schema In Fabric Warehouse, objects are commonly organized like this: Warehouse | |-- sales | |-- Customers | |-- Orders | |-- hr | |-- Employees | |-- finance |-- Transactions Schemas help you: Group related tables Manage permissions Organize large systems more effectively 2. Create a Schema Create a schema for the sales dataset: CREATE SCHEMA sales ; Check existing schemas: SELECT * FROM sys . schemas ; 3. Create Tables Create the Customers table: CREATE TABLE sales . Customers ( CustomerID INT , CustomerName VARCHAR ( 100 ), City VARCHAR ( 50 ), Country VARCHAR ( 50 ) ); Create the Orders table: CREATE TABLE sales . Orders ( OrderID INT , CustomerID INT , OrderDate DATE , Amount DECIMAL ( 10 , 2 ) ); 4. Insert Sample Data Customers INSERT INTO sales . Customers VALUES ( 1 , 'John Smith' , 'New York' , 'USA' ), ( 2 , 'Emma Brown' , 'Chicago' , 'USA' ), ( 3 , 'David Wilson' , 'London' , 'UK' ), ( 4 , 'Sophia Taylor' , 'Manchester' , 'UK' ), ( 5 , 'Michael Lee' , 'Singapore' , 'Singapore' ); Orders INSERT INTO sales . Orders VALUES ( 101 , 1 , '2026-01-10' , 1200 . 00 ), ( 102 , 1 , '2026-01-15' , 800 . 00 ), ( 103 , 2 , '2026-01-20' , 2500 . 00 ), ( 104 , 3 , '2026-02-01' , 500 . 00 ), ( 105 , 5 , '2026-02-05' , 3200 . 00 ); 5. SELECT Get all columns: SELECT * FROM sales . Customers ; Get specific columns: SELECT CustomerName , Country FROM sales . Customers ; 6. Alias Rename columns in the output: SELECT CustomerName AS Customer , Country AS Nation FROM sales . Customers ; 7. WHERE Filter rows using conditions. Customers in the USA: SELECT * FROM sales . Customers WHERE Country = 'USA' ; Orders greater than 100