Redo commits

site/sfguides/src/a_guide_to_implementing_a_pipeline_for_dcm/a_guide_to_implementing_a_pipeline_for_dcm.md

@@ -17,7 +17,6 @@ In this guide we will walkthrough how to use Snowflake Data Change Management fe
 ### Prerequisites
 - Familiarity with Snowflake, Github
 - A snowflake user with [Key Pair](https://docs.snowflake.com/en/user-guide/key-pair-auth), role sysadmin. 
-- A XSMALL snowflake warehouse. 
 - Setup Snowflake [cli connections](https://docs.snowflake.com/en/developer-guide/snowflake-cli/connecting/configure-connections)
 
 ### What You’ll Learn 
@@ -62,7 +61,7 @@ In this guide we will walkthrough how to use Snowflake Data Change Management fe
 
 ```shell
 openssl genrsa -out snowflake_demo_key 4096
-openssl rsa -in snowflake_demo_key -pubout -out snowflake_demo_key.pub
+openssl rsa -in snowflake_demo_key.pem -pubout -out snowflake_demo_key.pub
 openssl pkcs8 -topk8 -nocrypt -in snowflake_demo_key.pem -out snowflake_demo_key.p8
 ```
   + You could also use a [online service](https://www.cryptool.org/en/cto/openssl/) to create the key pair.
@@ -77,19 +76,33 @@ openssl pkcs8 -topk8 -nocrypt -in snowflake_demo_key.pem -out snowflake_demo_key
 
 ```sql
 USE ROLE ACCOUNTADMIN;
-CREATE OR REPLACE USER "DCM_DEMO" RSA_PUBLIC_KEY='RSA_PUBLIC_KEY_HERE' DEFAULT_ROLE=SYSADMIN MUST_CHANGE_PASSWORD=FALSE;
+CREATE OR REPLACE USER "DCM_DEMO" RSA_PUBLIC_KEY='RSA_PUBLIC_KEY_HERE' MUST_CHANGE_PASSWORD=FALSE;
+GRANT ROLE SYSADMIN TO USER DCM_DEMO;
 ```
 
-3) Use role SYSADMIN.
+3) Create warehouse and grant 
 
-4) Create database and schema to hold the objects that will be managed via DCM.
+```sql
+CREATE WAREHOUSE XSMALL
+  WITH WAREHOUSE_SIZE = XSMALL
+  AUTO_SUSPEND = 30
+  AUTO_RESUME = TRUE
+  COMMENT = 'This is a warehouse for dcm quickstart.';
+
+
+GRANT USAGE ON WAREHOUSE XSMALL TO ROLE SYSADMIN;
+```
+
+4) Use role SYSADMIN.
+
+5) Create database and schema to hold the objects that will be managed via DCM.
 
 ```sql
 create database DCM_PROJECTS;
 create schema DCM_PROJECTS.DCM_PROJECTS;
 ```
 
-5) Create Database, schema, tables for bronze layer. We will be using a subset of the tables/data.
+6) Create Database, schema, tables for bronze layer. We will be using a subset of the tables/data.
 
 ```sql
 create database bronze;
@@ -146,7 +159,7 @@ CREATE TABLE order_details (
 );
 
 ```
-6) Load the data
+7) Load the data
 
 ```sql
 INSERT INTO customers VALUES
@@ -349,11 +362,11 @@ jobs:
           --no-interactive --default
     - name: Create DCM Project if not exists 
       run: |
-        cd ./data_project && snow dcm create --if-not-exists proj1
+        cd ./dcm_project && snow dcm create --if-not-exists proj1
 
     - name: Execute PLAN with config PROD 
       run: |
-        cd ./data_project && snow dcm plan DCM.DCM.PROJ1 --configuration="prod"
+        cd ./dcm_project && snow dcm plan DCM.DCM.PROJ1 --configuration="prod"
 
 ```
 
@@ -402,13 +415,13 @@ jobs:
           --no-interactive --default
     - name: Create DCM Project if not exists 
       run: |
-        cd ./data_project && snow dcm create --if-not-exists proj1
+        cd ./dcm_project && snow dcm create --if-not-exists proj1
     - name: Execute PLAN with config PROD 
       run: |
-        cd ./data_project && snow dcm plan DCM.DCM.PROJ1 --configuration="prod"
+        cd ./dcm_project && snow dcm plan DCM.DCM.PROJ1 --configuration="prod"
     - name: Execute deploy with config prod
       run: |
-        cd ./data_project && snow dcm deploy DCM.DCM.PROJ1 --configuration="prod"
+        cd ./dcm_project && snow dcm deploy DCM.DCM.PROJ1 --configuration="prod"
 
 ```
 5) Add sandbox script and requirements.txt
@@ -486,7 +499,7 @@ SANITIZED_NAME=$(echo ${BRANCH_NAME//-/_} | tr '[:lower:]' '[:upper:]')
 
 pushd $REPO_ROOT
 snow sql -q "CREATE OR REPLACE STAGE ${SANITIZED_NAME}_FILES"
-snow stage copy --recursive ./data_project @${SANITIZED_NAME}_FILES/
+snow stage copy --recursive ./dcm_project @${SANITIZED_NAME}_FILES/
 snow sql -q "EXECUTE DCM PROJECT ${SANITIZED_NAME} PLAN USING CONFIGURATION non_prod (target_db => '${SANITIZED_NAME}', target_schema => '${SANITIZED_NAME}') FROM @${SANITIZED_NAME}_FILES/;"
 popd
 
@@ -502,7 +515,7 @@ SANITIZED_NAME=$(echo ${BRANCH_NAME//-/_} | tr '[:lower:]' '[:upper:]')
 
 pushd $REPO_ROOT
 snow sql -q "CREATE OR REPLACE STAGE ${SANITIZED_NAME}_FILES"
-snow stage copy --recursive ./data_project @${SANITIZED_NAME}_FILES/
+snow stage copy --recursive ./dcm_project @${SANITIZED_NAME}_FILES/
 
 snow sql -q "EXECUTE DCM PROJECT ${SANITIZED_NAME} DEPLOY USING CONFIGURATION non_prod (target_db => '${SANITIZED_NAME}', target_schema => '${SANITIZED_NAME}') FROM @${SANITIZED_NAME}_FILES/;"
 popd
@@ -579,12 +592,12 @@ urllib3==2.5.0
 wcwidth==0.2.13
 ```
 
-6) Create folder called data_project with manifest.yml
+6) Create folder called dcm_project with manifest.yml
 
 ```shell
 cd <your repo>
-mkdir -p data_project/definitions
-cd data_project
+mkdir -p dcm_project/definitions
+cd dcm_project
 ```
   + In your code editor create file manifest.yml. Copy following content into manifest.yml
 
@@ -616,11 +629,11 @@ git push origin main
 
 ## Setup DCM to manage the gold layer
 1) Create gold database and schema
-  + Add a sql file into the data_project/definitions folder called gold.sql.
+  + Add a sql file into the dcm_project/definitions folder called gold.sql.
 
 ```shell
 git checkout -b setup_gold
-cd data_project/definitions
+cd dcm_project/definitions
 ```
 
   + In your code editor create file gold.sql. Contents of gold.sql:
@@ -659,8 +672,8 @@ git pull
 cd sandbox
 ./do.sh create PROJ-001
 cd ..
-mkdir -p data_project/definitions
-cd data_project/definitions
+mkdir -p dcm_project/definitions
+cd dcm_project/definitions
 ```
 
   + do.sh creates a git branch named 'PROJ-001' and all changes are made in the context of this branch. It also creates a sandbox environment by cloning the gold layer.
@@ -723,8 +736,8 @@ git pull
 cd sandbox
 ./do.sh create PROJ-002
 cd ..
-mkdir -p data_project/definitions
-cd data_project/definitions
+mkdir -p dcm_project/definitions
+cd dcm_project/definitions
 ```
 
   + In your code editor create file order_fact.sql. Copy following content into order_fact.sql
@@ -809,6 +822,7 @@ In this guide we targetted a subset of tables in the northwind database.
   + You can have the main branch map to the prod snowflake account, and an integration branch can be mapped
    to the non-prod snowflake account.
   + The sandbox creation script can be hosted in a CI system like Jenkins, etc
+  + You can enhance the worklfow by adding Expectations attached to tables in project and using the TEST ALL command to 
 
 
 5) All scripts/shell commands in this quickstart are tested to work on macOS.

site/sfguides/src/cortex_ai_demo_framework/cortex_ai_demo_framework.md

@@ -25,6 +25,8 @@ This Quickstart showcases the complete Cortex AI Demo Framework with:
 - **Production-ready applications** with professional UI/UX
 
 
+![Architecture Diagram](assets/architecture_diagram.png)
+
 ### What You Will Build
 - Complete 6-application integrated demo platform
 - AI-powered synthetic data generation system using Cortex functions
@@ -1259,8 +1261,6 @@ Your demo is complete! You can:
 - Edit YAML to add more steps or visualizations
 - Share demo with colleagues by sharing the YAML file
 
-**Return to Page 5** to explore other workflows or **continue to Page 12** for cleanup instructions.
-
 <!-- ------------------------ -->
 ## YAML Wizard
 
@@ -1806,18 +1806,6 @@ Shows complete dataset in table format with sortable columns and CSV export opti
 - Compare entities side-by-side
 - Export data for presentations
 
----
-
-### Best Practices
-
-**Explore systematically**: Start with Overview, then drill into specific tabs  
-**Use AI Assistant**: Natural language queries are powerful and intuitive  
-**Compare entities**: VS tab helps identify top performers  
-**Export insights**: Share findings via CSV export  
-**Adjust time windows**: Find the right time range for your analysis
-
----
-
 ### What's Next?
 
 **For Persona 1 (Full-Stack Developer)**:
@@ -1838,8 +1826,6 @@ You now have an interactive analytics dashboard! You can:
 - Compare products/customers/categories
 - Export data for presentations
 
-**Return to Page 5** to explore other workflows or **continue to Page 12** for cleanup instructions.
-
 <!-- ------------------------ -->
 ## Clean Up Resources
 

site/sfguides/src/dbt-projects-on-snowflake/dbt-projects-on-snowflake.md

@@ -35,14 +35,6 @@ In this lab, we will go through everything you need to know to get started with
 
 We will be using Tasty Bytes data in this lab. Run the script [here](https://github.com/Snowflake-Labs/getting-started-with-dbt-on-snowflake/blob/main/tasty_bytes_dbt_demo/setup/tasty_bytes_setup.sql) in Snowsight to build the objects and data required for this lab.
 
-Workspaces that you create in Snowflake are created in the personal database associated with the active user. To use Workspaces, you must run the following SQL commands to activate all secondary roles for your user.
-
-``` sql
-ALTER USER my_user SET DEFAULT_SECONDARY_ROLES = ('ALL');
-```
-
-Sign out of Snowsight and sign back in.
-
 <!-- ------------------------ -->
 ## Introduction to Workspaces
 
@@ -96,7 +88,7 @@ Let's now clone an example dbt project we will use in the rest of this lab.
     1. Repository URL: `https://github.com/Snowflake-Labs/getting-started-with-dbt-on-snowflake.git`
     2. Workspace Name: `Example-dbt-Project`
     3. API Integration: `GIT_INTEGRATION`. Note: the [API Integration](https://docs.snowflake.com/en/developer-guide/git/git-setting-up#label-integrating-git-repository-api-integration) has already been configured for you. 
-    4. Select Public Repository
+    4. Select Public Repository. Note: Private repos can be authenticated with personal access tokens and GitHub users can authenticate with [OAuth](https://docs.snowflake.com/en/developer-guide/git/git-setting-up#configure-for-authenticating-with-oauth).
 3. Click Create!
 
 ![create-dbt-project](assets/create-workspace-git.png)
@@ -168,7 +160,7 @@ Let's start by running `dbt deps` to pull in the dbt_utils package. The dbt_util
 From the dbt toolbar, you get dropdowns for the project, target, and command. Clicking the play button will run the relevant command. You can also click the down arrow to override the arguments. 
 
 1. From the toolbar, select dev and deps. 
-2. Click the dropdown arrow and enter `dbt_access_integration`. This [external access integration](https://docs.snowflake.com/en/sql-reference/sql/create-external-access-integration) has already been configured for you. 
+2. Click the dropdown arrow and select `dbt_access_integration`. This [external access integration](https://docs.snowflake.com/en/sql-reference/sql/create-external-access-integration) has already been configured for you. 
 3. Click the Deps button.
 
 ![dbt-deps](assets/dbt-deps.png)
@@ -228,23 +220,23 @@ Workspaces are fully git backed. To view changes and commit, click changes from
 <!-- ------------------------ -->
 ## Orchestration and Monitoring
 
-### Monitor dbt Projects
+### Orchestrate with Tasks
 
-You can get an overview of dbt project status from the dbt Projects activity in Snowsight. Navigate to monitoring > dbt Projects to view overall status of dbt Projects and quickly jump to the deployed projects.
+Navigate to Catalog > Database Explorer > TASTY_BYTES_DBT_DB > RAW > dbt Projects > DBT_PROJECT to view the project details. From the Run History tab, you can view all runs associated with the project. 
 
-![dbt-projects](assets/dbt-projects.png)
-
-### Orchestrate with Tasks
+![project-details](assets/dbt_project_details.png)
 
 #### Create Scheduled dbt Tasks
 
 Let's create tasks to regularly run and test our dbt project. 
 
-1. Click dbt_project in the top right corner of Workspaces
-2. Click Create Schedule from the dropdown
+1. Navigate to the Project Details tab
+2. Click Create Schedule from the Schedules dropdown
 3. Enter a name, schedule, and profile, then click create
 
-![create-task](assets/create-task.png)
+![create-schedule](assets/dbt-create-schedule.png)
+
+![create-task](assets/create-schedule.png)
 
 #### Complex Tasks and Alerts
 
@@ -263,18 +255,7 @@ CREATE OR REPLACE TASK tasty_bytes_dbt_db.raw.dbt_run_task
 CREATE OR REPLACE TASK tasty_bytes_dbt_db.raw.dbt_test_task
 	WAREHOUSE=TASTY_BYTES_DBT_WH
 	AFTER tasty_bytes_dbt_db.raw.dbt_run_task
-	AS
-        DECLARE 
-            dbt_success BOOLEAN;
-            dbt_exception STRING;
-            my_exception EXCEPTION (-20002, 'My exception text');
-        BEGIN
-            EXECUTE DBT PROJECT "TASTY_BYTES_DBT_DB"."RAW"."DBT_PROJECT" args='test --target dev';
-            SELECT SUCCESS, EXCEPTION into :dbt_success, :dbt_exception FROM TABLE(result_scan(last_query_id()));
-            IF (NOT :dbt_success) THEN
-              raise my_exception;
-            END IF;
-        END;
+	AS EXECUTE DBT PROJECT "TASTY_BYTES_DBT_DB"."RAW"."DBT_PROJECT" args='test --target dev';
 
 -- Run the tasks once
 ALTER TASK tasty_bytes_dbt_db.raw.dbt_test_task RESUME;
@@ -326,6 +307,12 @@ You can view the status or running tasks by going to Monitoring > Task History.
 
 ![tasks](assets/tasks.png)
 
+### Monitor dbt Projects
+
+You can get an overview of dbt project status from the dbt Projects activity in Snowsight. Navigate to Monitoring > dbt Projects to view overall status of dbt Projects and quickly jump to the deployed projects.
+
+![dbt-projects](assets/dbt-projects.png)
+
 ### Tracing
 
 dbt projects in Snowflake integrate with [Tracing and Logging](https://docs.snowflake.com/en/developer-guide/logging-tracing/logging-tracing-overview), allowing you to easily monitor and debug dbt projects. Tracing follows the OpenTelemetry standard and allows you to keep logs within a single platform. 

site/sfguides/src/getting_started_with_interactive_tables/README.md

@@ -0,0 +1,26 @@
+# Getting Started with Snowflake Interactive Tables
+
+## Overview
+
+When it comes to near real-time (or sub-second) analytics, the ideal scenario involves achieving consistent, rapid query performance and managing costs effectively, even with large datasets and high user demand. 
+
+Snowflake's new Interactive Warehouses and Tables are designed to deliver on these needs. They provide high-concurrency, low-latency serving layer for near real-time analytics. This allows consistent, sub-second query performance for live dashboards and APIs with great price-for-performance. With this end-to-end solution, you can avoid operational complexities and tool sprawl.
+
+Here's how interactive warehouses and tables fits in for a typical data analytics pipeline:
+
+![](assets/architecture.png)
+
+### What You'll Learn
+- The core concepts behind Snowflake's Interactive Warehouses and Tables and how they provide low-latency analytics.
+- How to create and configure an Interactive Warehouse using SQL.
+- The process of creating an Interactive Table from an existing standard table.
+- How to attach a table to an Interactive Warehouse to pre-warm the data cache for faster queries.
+- A methodology for benchmarking and comparing the query latency of an interactive setup versus a standard warehouse.
+
+### What You'll Build
+
+You will build a complete, functioning interactive data environment in Snowflake, including a dedicated Interactive Warehouse and an Interactive Table populated with data. You will also create a Python-based performance test that executes queries against both your new interactive setup and a standard configuration, culminating in a comparative bar chart that visually proves the latency improvements.
+
+# Step-By-Step Guide
+
+For prerequisites, environment setup, step-by-step guide and instructions, please refer to the [QuickStart Guide](template.md).