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updated notebook
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‎README.md‎

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@@ -93,8 +93,6 @@ from sempy_labs import lakehouse as lake
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from sempy_labs import report as rep
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from sempy_labs.tom import connect_semantic_model
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from sempy_labs.report import ReportWrapper
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from sempy_labs import ConnectWarehouse
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from sempy_labs import ConnectLakehouse
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```
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## Load Semantic Link Labs into a custom [Fabric environment](https://learn.microsoft.com/fabric/data-engineering/create-and-use-environment)

‎notebooks/SQL.ipynb‎

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{"cells":[{"cell_type":"markdown","id":"5c27dfd1-4fe0-4a97-92e6-ddf78889aa93","metadata":{"nteract":{"transient":{"deleting":false}}},"source":["### Install the latest .whl package\n","\n","Check [here](https://pypi.org/project/semantic-link-labs/) to see the latest version."]},{"cell_type":"code","execution_count":null,"id":"d5cae9db-cef9-48a8-a351-9c5fcc99645c","metadata":{"jupyter":{"outputs_hidden":true,"source_hidden":false},"nteract":{"transient":{"deleting":false}}},"outputs":[],"source":["%pip install semantic-link-labs"]},{"cell_type":"markdown","id":"b195eae8","metadata":{},"source":["### Import the library and necessary packages"]},{"cell_type":"code","execution_count":null,"id":"1344e286","metadata":{},"outputs":[],"source":["import sempy_labs as labs\n","from sempy_labs import ConnectWarehouse\n","from sempy_labs import ConnectLakehouse\n","\n","lakehouse_name = ''\n","lakehouse_workspace_name = ''\n","warehouse_name = ''\n","warehouse_workspace_name = ''"]},{"cell_type":"markdown","id":"55e5ca67","metadata":{},"source":["### Run a SQL query (or queries) against a Fabric warehouse"]},{"cell_type":"code","execution_count":null,"id":"a9f984e9","metadata":{},"outputs":[],"source":["with ConnectWarehouse(warehouse=warehouse_name, workspace=warehouse_workspace_name) as sql:\n"," df = sql.query(\"SELECT * FROM Product\")\n"," display(df)"]},{"cell_type":"code","execution_count":null,"id":"865ac4a1","metadata":{},"outputs":[],"source":["with ConnectWarehouse(warehouse=warehouse_name, workspace=warehouse_workspace_name) as sql:\n"," dfs = sql.query([\"SELECT * FROM Product\", \"SELECT DISTINCT [Category] FROM Product\"])\n","\n","for df in dfs:\n"," display(df)"]},{"cell_type":"markdown","id":"bca53cd8","metadata":{},"source":["#### See the tables in a warehouse"]},{"cell_type":"code","execution_count":null,"id":"9af2cce7","metadata":{},"outputs":[],"source":["labs.get_warehouse_tables(warehouse=warehouse_name, workspace=warehouse_workspace_name)"]},{"cell_type":"markdown","id":"765f99ae","metadata":{},"source":["#### See the columns in each table in a warehouse"]},{"cell_type":"code","execution_count":null,"id":"1fabe168","metadata":{},"outputs":[],"source":["labs.get_warehouse_columns(warehouse=warehouse_name, workspace=warehouse_workspace_name)"]},{"cell_type":"markdown","id":"634700c3","metadata":{},"source":["### Run a T-SQL query (or queries) against a Fabric warehouse"]},{"cell_type":"code","execution_count":null,"id":"5dbf34f3","metadata":{},"outputs":[],"source":["with ConnectWarehouse(warehouse=warehouse_name, workspace=warehouse_workspace_name) as sql:\n"," sql.query(\"CREATE SCHEMA [Business]\")"]},{"cell_type":"code","execution_count":null,"id":"ec8ddb81","metadata":{},"outputs":[],"source":["with ConnectWarehouse(warehouse=warehouse_name, workspace=warehouse_workspace_name) as sql:\n"," sql.query([\"CREATE SCHEMA [Business]\", \"CREATE SCHEMA [Marketing]\"])"]},{"cell_type":"markdown","id":"d5b090da","metadata":{},"source":["### Run a SQL query (or queries) against a Fabric lakehouse"]},{"cell_type":"code","execution_count":null,"id":"4dca7f4a","metadata":{},"outputs":[],"source":["with ConnectLakehouse(lakehouse=lakehouse_name, workspace=lakehouse_workspace_name) as sql:\n"," df = sql.query(\"SELECT * FROM Product\")\n"," display(df)"]},{"cell_type":"code","execution_count":null,"id":"b9606ae8","metadata":{},"outputs":[],"source":["with ConnectLakehouse(lakehouse=lakehouse_name, workspace=lakehouse_workspace_name) as sql:\n"," dfs = sql.query([\"SELECT * FROM Product\", \"SELECT DISTINCT [Category] FROM Product\"])\n","\n","for df in dfs:\n"," display(df)"]}],"metadata":{"kernel_info":{"name":"synapse_pyspark"},"kernelspec":{"display_name":"Synapse PySpark","language":"Python","name":"synapse_pyspark"},"language_info":{"name":"python"},"microsoft":{"language":"python"},"nteract":{"version":"[email protected]"},"spark_compute":{"compute_id":"/trident/default"},"synapse_widget":{"state":{},"version":"0.1"},"widgets":{}},"nbformat":4,"nbformat_minor":5}
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "5c27dfd1-4fe0-4a97-92e6-ddf78889aa93",
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"metadata": {
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"nteract": {
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"transient": {
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"deleting": false
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}
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}
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},
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"source": [
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"### Install the latest .whl package\n",
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"\n",
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"Check [here](https://pypi.org/project/semantic-link-labs/) to see the latest version."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d5cae9db-cef9-48a8-a351-9c5fcc99645c",
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"metadata": {
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"jupyter": {
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"outputs_hidden": true,
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"source_hidden": false
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},
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"nteract": {
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"transient": {
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"deleting": false
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}
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}
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},
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"outputs": [],
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"source": [
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"%pip install semantic-link-labs"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b195eae8",
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"metadata": {},
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"source": [
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"### Import the library and necessary packages"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1344e286",
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"metadata": {},
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"outputs": [],
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"source": [
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"import sempy_labs as labs\n",
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"\n",
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"lakehouse_name = ''\n",
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"lakehouse_workspace_name = ''\n",
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"warehouse_name = ''\n",
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"warehouse_workspace_name = ''"
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]
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},
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{
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"cell_type": "markdown",
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"id": "55e5ca67",
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"metadata": {},
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"source": [
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"### Run a SQL query (or queries) against a Fabric warehouse"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a9f984e9",
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"metadata": {},
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"outputs": [],
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"source": [
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"with labs.ConnectWarehouse(warehouse=warehouse_name, workspace=warehouse_workspace_name) as sql:\n",
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" df = sql.query(\"SELECT * FROM Product\")\n",
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" display(df)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "865ac4a1",
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"metadata": {},
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"outputs": [],
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"source": [
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"with labs.ConnectWarehouse(warehouse=warehouse_name, workspace=warehouse_workspace_name) as sql:\n",
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" dfs = sql.query([\"SELECT * FROM Product\", \"SELECT DISTINCT [Category] FROM Product\"])\n",
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"\n",
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"for df in dfs:\n",
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" display(df)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bca53cd8",
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"metadata": {},
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"source": [
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"#### See the tables in a warehouse"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9af2cce7",
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"metadata": {},
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"outputs": [],
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"source": [
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"labs.get_warehouse_tables(warehouse=warehouse_name, workspace=warehouse_workspace_name)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "765f99ae",
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"metadata": {},
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"source": [
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"#### See the columns in each table in a warehouse"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1fabe168",
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"metadata": {},
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"outputs": [],
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"source": [
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"labs.get_warehouse_columns(warehouse=warehouse_name, workspace=warehouse_workspace_name)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "634700c3",
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"metadata": {},
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"source": [
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"### Run a T-SQL query (or queries) against a Fabric warehouse"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5dbf34f3",
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"metadata": {},
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"outputs": [],
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"source": [
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"with labs.ConnectWarehouse(warehouse=warehouse_name, workspace=warehouse_workspace_name) as sql:\n",
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" sql.query(\"CREATE SCHEMA [Business]\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ec8ddb81",
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"metadata": {},
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"outputs": [],
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"source": [
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"with labs.ConnectWarehouse(warehouse=warehouse_name, workspace=warehouse_workspace_name) as sql:\n",
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" sql.query([\"CREATE SCHEMA [Business]\", \"CREATE SCHEMA [Marketing]\"])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d5b090da",
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"metadata": {},
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"source": [
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"### Run a SQL query (or queries) against a Fabric lakehouse"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4dca7f4a",
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"metadata": {},
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"outputs": [],
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"source": [
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"with labs.ConnectLakehouse(lakehouse=lakehouse_name, workspace=lakehouse_workspace_name) as sql:\n",
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" df = sql.query(\"SELECT * FROM Product\")\n",
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" display(df)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b9606ae8",
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"metadata": {},
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"outputs": [],
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"source": [
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"with labs.ConnectLakehouse(lakehouse=lakehouse_name, workspace=lakehouse_workspace_name) as sql:\n",
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" dfs = sql.query([\"SELECT * FROM Product\", \"SELECT DISTINCT [Category] FROM Product\"])\n",
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"\n",
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"for df in dfs:\n",
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" display(df)"
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]
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}
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],
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"metadata": {
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"kernel_info": {
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"name": "synapse_pyspark"
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},
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"kernelspec": {
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"display_name": "Synapse PySpark",
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"language": "Python",
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"name": "synapse_pyspark"
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},
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"language_info": {
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"name": "python"
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},
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"microsoft": {
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"language": "python"
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},
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"nteract": {
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"version": "[email protected]"
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},
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"spark_compute": {
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"compute_id": "/trident/default"
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},
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"synapse_widget": {
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"state": {},
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"version": "0.1"
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},
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"widgets": {}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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