use virtual environment name (#725)

Author: eakmanrq

docs/comparisons.md

@@ -62,7 +62,7 @@ dbt's default full refresh approach leads to the most costly version of this loo
 
 SQLMesh takes another approach. It examines the code modifications and the dependency structure among the models to determine which models are affected -- and executes only those models. This results in the least costly version of the loop: computing only what is required every time through.
 
-This enables SQLMesh to provide efficient isolated environments with [Virtual Data Marts](./concepts/plans.md#plan-application). Environments in dbt cost compute and storage, but creating a development environment in SQLMesh is free -- you can quickly access a full replica of any other environment with a single command.
+This enables SQLMesh to provide efficient isolated [Virtual Environments](./concepts/plans.md#plan-application). Environments in dbt cost compute and storage, but creating a development environment in SQLMesh is free -- you can quickly access a full replica of any other environment with a single command.
 
 Additionally, SQLMesh ensures that promotion of staging environments to production is predictable and consistent. There is no concept of promotion in dbt, so queries are all rerun when it's time to deploy something. In SQLMesh, promotions are simple pointer swaps so there is no wasted compute.
 

docs/concepts/glossary.md

@@ -66,8 +66,8 @@ Functions that a user of a database server provides to extend its functionality,
 ## View
 A view is the result of a SQL query on a database.
 
-## Virtual Data Marts
-Term used to describes SQLMesh's ability to share tables across environments to ensure tables are only built once while maintaining data integrity and environment isolation. See [plan application](plans.md#plan-application) for more information. 
+## Virtual Environments
+SQLMesh's unique approach to environment that allows it to provide both environment isolation and the ability to share tables across environments. This is done in a way to ensure data consistency and accuracy. See [plan application](plans.md#plan-application) for more information. 
 
 ## Virtual Update
 Term used to describe a plan that can be applied without having to load any additional data or build any additional tables. See [Virtual Update](plans.md#virtual-update) for more information.

docs/concepts/overview.md

@@ -35,16 +35,16 @@ Most business data is temporal — each data fact was collected at a specifi
 
 The SQLMesh plan automatically determines which models and dates require backfill due to your changes. Based on this information, you specify the dates for which backfills will occur before you apply the plan.
 
-#### Build a virtual data mart
+#### Build a Virtual Environment
 Development activities for complex data systems should occur in a non-production environment so that errors can be detected before being deployed in production systems.
 
 One challenge with using multiple data environments is that backfill and other computations must happen twice — once for the non-production, and again for the production environment. This process consumes time and computing resources, resulting in delays and extra costs.
 
 SQLMesh solves this problem by maintaining a record of all model versions and their changes. It uses this record to determine when computations executed in a non-production environment generate outputs identical to what they would generate in the production environment. 
 
-SQLMesh uses its knowledge of equivalent outputs to create a **virtual data mart**. It does this by replacing references to outdated tables in the production environment with references to newly computed tables in the non-production environment. It effectively promotes views and tables from non-production to production, but *without computation or data movement*.
+SQLMesh uses its knowledge of equivalent outputs to create a **Virtual Environment**. It does this by replacing references to outdated tables in the production environment with references to newly computed tables in the non-production environment. It effectively promotes views and tables from non-production to production, but *without computation or data movement*.
 
-Because SQLMesh uses virtual data marts instead of re-computing everything in the production environment, promoting changes to production is quick and has no downtime. 
+Because SQLMesh uses virtual environments instead of re-computing everything in the production environment, promoting changes to production is quick and has no downtime. 
 
 ## Test your code and data
 Bad data is worse than no data. The best way to keep bad data out of your system is by testing your transformation code and results.

docs/concepts/plans.md

@@ -41,7 +41,7 @@ When a plan is applied to an environment, that environment gets associated with
 
 *Each model variant gets its own physical table while environments only contain references to these tables.*
 
-This unique approach to understanding and applying changes is what enables SQLMesh's virtual data mart technology. This technology allows SQLMesh to ensure complete isolation between environments while allowing it to share physical data assets between environments when appropriate and safe to do so. Additionally, since each model change is captured in a separate physical table, reverting to a previous version becomes a simple and quick operation (refer to [Virtual Update](#virtual-update)) as long as its physical table hasn't been garbage collected by the janitor process. SQLMesh makes it easy to be correct, and really hard to accidentally and irreversibly break things.
+This unique approach to understanding and applying changes is what enables SQLMesh's Virtual Environments. This technology allows SQLMesh to ensure complete isolation between environments while allowing it to share physical data assets between environments when appropriate and safe to do so. Additionally, since each model change is captured in a separate physical table, reverting to a previous version becomes a simple and quick operation (refer to [Virtual Update](#virtual-update)) as long as its physical table hasn't been garbage collected by the janitor process. SQLMesh makes it easy to be correct, and really hard to accidentally and irreversibly break things.
 
 ### Backfilling
 Despite all the benefits, the approach described above is not without trade-offs. When a new model version is just created, a physical table assigned to it is empty. Therefore, SQLMesh needs to re-apply the logic of the new model version to the entire date range of this model in order to populate the new version's physical table. This process is called backfilling.

docs/guides/projects.md

@@ -24,7 +24,7 @@ To create a project from the command line, follow these steps:
 
     From here, you can create your project structure from scratch, or SQLMesh can scaffold one for you. For the purposes of this guide, we'll show you how to scaffold your project so that you can get up and running quickly.
 
-1. To scaffold a project, it is recommended that you use a virtual environment by running the following commands:
+1. To scaffold a project, it is recommended that you use a python virtual environment by running the following commands:
 
     ```bash
     python -m venv .env
@@ -38,7 +38,7 @@ To create a project from the command line, follow these steps:
     pip install sqlmesh
     ```
 
-    **Note:** When using a virtual environment, you must ensure that it is activated first. You should see `(.env)` in your command line; if you don't, run `source .env/bin/activate` from your project directory to activate your environment.
+    **Note:** When using a python virtual environment, you must ensure that it is activated first. You should see `(.env)` in your command line; if you don't, run `source .env/bin/activate` from your project directory to activate your environment.
 
 1. Once you have activated your environment, run the following command and SQLMesh will build out your project:
 

docs/index.md

@@ -37,7 +37,7 @@ SQLMesh was built on three core principles:
 
 ### Key features
 * Efficient dev/staging environments
-    * SQLMesh builds a virtual data mart using views, which allows you to seamlessly rollback or roll forward your changes. Any data computation you run for validation purposes is actually not wasted — with a cheap pointer swap, you re-use your “staging” data in production. This means you get unlimited copy-on-write environments that make data exploration and preview of changes fun and safe.
+    * SQLMesh builds a Virtual Data Environment using views, which allows you to seamlessly rollback or roll forward your changes. Any data computation you run for validation purposes is actually not wasted — with a cheap pointer swap, you re-use your “staging” data in production. This means you get unlimited copy-on-write environments that make data exploration and preview of changes fun and safe.
 
 * Automatic DAG generation by semantically parsing and understanding SQL or Python scripts
     * No need to manually tag dependencies — SQLMesh was built with the ability to understand your entire data warehouse’s dependency graph.

docs/installation.md

@@ -1,7 +1,7 @@
 # Installation
 
 
-It is recommended, but not required, that you use a virtual environment:
+It is recommended, but not required, that you use a python virtual environment:
 ```bash
 python -m venv .env
 ```

docs/integrations/github.md

@@ -2,7 +2,7 @@
 
 SQLMesh's Github Actions integration will allow you to add a SQLMesh CI/CD bot to any Github project using [Github Actions](https://github.com/features/actions). The bot will automatically run [plan/apply](../concepts/plans.md) to an [environment](../concepts/environments.md) based on the code in a pull request.
 
-This will be done without copying or rebuilding data using SQLMesh's [Virtual Data Mart technology](../concepts/glossary.md#virtual-data-marts). 
+This will be done without copying or rebuilding data using SQLMesh's [Virtual Data Environments](../concepts/glossary.md#virtual-environments). 
 Once approved, the CI/CD bot will automatically run [plan/apply](../concepts/plans.md) to the production environment and merge the PR upon completion.
 This allows you to always have your main branch and prod environments in sync.
 

docs/quick_start.md

@@ -13,7 +13,7 @@ mkdir sqlmesh-example
 cd sqlmesh-example
 ```
 
-When using a virtual environment, you must ensure it's activated first by running the `source .env/bin/activate` command from the folder used during [installation](installation.md).
+When using a python virtual environment, you must ensure it's activated first by running the `source .env/bin/activate` command from the folder used during [installation](installation.md).
 
 Create a SQLMesh scaffold by using the following command: