Update pip syntax

website/blog/2021-11-29-open-source-community-growth.md

@@ -57,7 +57,7 @@ For starters, I want to know how much conversation is occurring across the vario
 
 There are a ton of metrics that can be tracked in any GitHub project — committers, pull requests, forks, releases — but I started pretty simple. For each of the projects we participate in, I just want to know how the number of GitHub stars grows over time, and whether the growth is accelerating or flattening out. This has become a key performance indicator for open source communities, for better or for worse, and keeping track of it isn't optional.
 
-Finally, I want to know how much Marquez and OpenLineage are being used. It used to be that when you wanted to consume a bit of tech, you'd download a file. Folks like me who study user behavior would track download counts as if they were stock prices. This is no longer the case; today, our tech is increasingly distributed through package managers and image repositories. Docker Hub and PyPI metrics have therefore become good indicators of consumption. Docker image pulls and runs of `pip install` are the modern day download and, as noisy as these metrics are, they indicate a similar level of user commitment.
+Finally, I want to know how much Marquez and OpenLineage are being used. It used to be that when you wanted to consume a bit of tech, you'd download a file. Folks like me who study user behavior would track download counts as if they were stock prices. This is no longer the case; today, our tech is increasingly distributed through package managers and image repositories. Docker Hub and PyPI metrics have therefore become good indicators of consumption. Docker image pulls and runs of `python -m pip install` are the modern day download and, as noisy as these metrics are, they indicate a similar level of user commitment.
 
 To summarize, here are the metrics I decided to track (for now, anyway):
 - Slack messages (by user/ by community)

website/blog/2022-04-14-add-ci-cd-to-bitbucket.md

@@ -159,7 +159,7 @@ pipelines:
           artifacts:  # Save the dbt run artifacts for the next step (upload)
             - target/*.json
           script:
-            - pip install -r requirements.txt
+            - python -m pip install -r requirements.txt
             - mkdir ~/.dbt
             - cp .ci/profiles.yml ~/.dbt/profiles.yml
             - dbt deps
@@ -208,7 +208,7 @@ pipelines:
             # Set up dbt environment + dbt packages. Rather than passing
             # profiles.yml to dbt commands explicitly, we'll store it where dbt
             # expects it:
-            - pip install -r requirements.txt
+            - python -m pip install -r requirements.txt
             - mkdir ~/.dbt
             - cp .ci/profiles.yml ~/.dbt/profiles.yml
             - dbt deps

website/blog/2022-05-03-making-dbt-cloud-api-calls-using-dbt-cloud-cli.md

@@ -59,7 +59,7 @@ You probably agree that the latter example is definitely more elegant and easier
 
 In addition to CLI commands that interact with a single dbt Cloud API endpoint there are composite helper commands that call one or more API endpoints and perform more complex operations. One example of composite commands are `dbt-cloud job export` and `dbt-cloud job import` where, under the hood, the export command performs a `dbt-cloud job get` and writes the job metadata to a <Term id="json" /> file and the import command reads job parameters from a JSON file and calls `dbt-cloud job create`. The export and import commands can be used in tandem to move dbt Cloud jobs between projects. Another example is the `dbt-cloud job delete-all` which fetches a list of all jobs using `dbt-cloud job list` and then iterates over the list prompting the user if they want to delete the job. For each job that the user agrees to delete  a `dbt-cloud job delete` is performed.
 
-To install the CLI in your Python environment run `pip install dbt-cloud-cli` and you’re all set. You can use it locally in your development environment or e.g. in a GitHub actions workflow.
+To install the CLI in your Python environment run `python -m pip install dbt-cloud-cli` and you’re all set. You can use it locally in your development environment or e.g. in a GitHub actions workflow.
 
 ## How the project came to be
 
@@ -310,7 +310,7 @@ The `CatalogExploreCommand.execute` method implements the interactive exploratio
 I’ve included the app in the latest version of dbt-cloud-cli so you can test it out yourself! To use the app you need install dbt-cloud-cli with extra dependencies:
 
 ```bash
-pip install dbt-cloud-cli[demo]
+python -m pip install dbt-cloud-cli[demo]
 ```
 
 Now you can the run app:

website/blog/2023-04-18-building-a-kimball-dimensional-model-with-dbt.md

@@ -79,12 +79,12 @@ Depending on which database you’ve chosen, install the relevant database adapt
 
 ```text
 # install adaptor for duckdb
-pip install dbt-duckdb
+python -m pip install dbt-duckdb
 
 # OR 
 
 # install adaptor for postgresql
-pip install dbt-postgres
+python -m pip install dbt-postgres
 ```
 
 ### Step 4: Setup dbt profile

website/docs/best-practices/how-we-build-our-metrics/semantic-layer-2-setup.md

@@ -23,8 +23,8 @@ We'll use pip to install MetricFlow and our dbt adapter:
 python -m venv [virtual environment name]
 source [virtual environment name]/bin/activate
 # install dbt and MetricFlow
-pip install "dbt-metricflow[adapter name]"
-# e.g. pip install "dbt-metricflow[snowflake]"
+python -m pip install "dbt-metricflow[adapter name]"
+# e.g. python -m pip install "dbt-metricflow[snowflake]"
 ```
 
 Lastly, to get to the pre-Semantic Layer starting state, checkout the `start-here` branch.

website/docs/docs/build/metricflow-commands.md

@@ -17,7 +17,7 @@ MetricFlow is compatible with Python versions 3.8, 3.9, 3.10, and 3.11.
 
 MetricFlow is a dbt package that allows you to define and query metrics in your dbt project. You can use MetricFlow to query metrics in your dbt project in the dbt Cloud CLI, dbt Cloud IDE, or dbt Core.
 
-**Note** — MetricFlow commands aren't supported in dbt Cloud jobs yet. However, you can add MetricFlow validations with your git provider (such as GitHub Actions) by installing MetricFlow (`pip install metricflow`). This allows you to run MetricFlow commands as part of your continuous integration checks on PRs.
+**Note** — MetricFlow commands aren't supported in dbt Cloud jobs yet. However, you can add MetricFlow validations with your git provider (such as GitHub Actions) by installing MetricFlow (`python -m pip install metricflow`). This allows you to run MetricFlow commands as part of your continuous integration checks on PRs.
 
 <Tabs>
 
@@ -54,7 +54,7 @@ You can install [MetricFlow](https://github.com/dbt-labs/metricflow#getting-star
 
 1. Create or activate your virtual environment `python -m venv venv`
 2. Run `pip install dbt-metricflow`
-  * You can install MetricFlow using PyPI as an extension of your dbt adapter in the command line. To install the adapter, run `pip install "dbt-metricflow[your_adapter_name]"` and add the adapter name at the end of the command. For example, for a Snowflake adapter run `pip install "dbt-metricflow[snowflake]"`
+  * You can install MetricFlow using PyPI as an extension of your dbt adapter in the command line. To install the adapter, run `python -m pip install "dbt-metricflow[your_adapter_name]"` and add the adapter name at the end of the command. For example, for a Snowflake adapter run `python -m pip install "dbt-metricflow[snowflake]"`
 
 **Note**, you'll need to manage versioning between dbt Core, your adapter, and MetricFlow.
 

website/docs/docs/cloud/cloud-cli-installation.md

@@ -155,13 +155,13 @@ If you already have dbt Core installed, the dbt Cloud CLI may conflict. Here are
   - Uninstall the dbt Cloud CLI using the command: `pip uninstall dbt`
   - Reinstall dbt Core using the following command, replacing "adapter_name" with the appropriate adapter name:
     ```shell
-    pip install dbt-adapter_name --force-reinstall
+    python -m pip install dbt-adapter_name --force-reinstall
     ```
-    For example, if I used Snowflake as an adapter, I would run: `pip install dbt-snowflake --force-reinstall`
+    For example, if I used Snowflake as an adapter, I would run: `python -m pip install dbt-snowflake --force-reinstall`
 
 --------
 
-Before installing the dbt Cloud CLI, make sure you have Python installed and your virtual environment venv or pyenv . If you already have a Python environment configured, you can skip to the [pip installation step](#install-dbt-cloud-cli-in-pip).
+Before installing the dbt Cloud CLI, make sure you have Python installed and your virtual environment venv or pyenv . If you already have a Python environment configured, you can skip to the [python -m pip installation step](#install-dbt-cloud-cli-in-pip).
 
 ### Install a virtual environment
 
@@ -200,7 +200,7 @@ We recommend using virtual environments (venv) to namespace `cloud-cli`.
 
   ```bash
   pip3 uninstall dbt-core dbt
-  pip install dbt-adapter_name --force-reinstall
+  python -m pip install dbt-adapter_name --force-reinstall
   ```
 
 4. Clone your repository to your local computer using `git clone`. For example, to clone a GitHub repo using HTTPS format, run `git clone https://github.com/YOUR-USERNAME/YOUR-REPOSITORY`.
@@ -243,7 +243,7 @@ To update, follow the same process explained in [Windows](/docs/cloud/cloud-cli-
 
 To update:
 - Make sure you're in your virtual environment
-- Run `pip install --upgrade dbt`.
+- Run `python -m pip install --upgrade dbt`.
 
 </TabItem>
 

website/docs/docs/connect-adapters.md

@@ -15,7 +15,7 @@ Explore the fastest and most reliable way to deploy dbt using dbt Cloud, a hoste
 
 Install dbt Core, an open-source tool, locally using the command line. dbt communicates with a number of different data platforms by using a dedicated  adapter plugin for each. When you install dbt Core, you'll also need to install the specific adapter for your database, [connect to dbt Core](/docs/core/about-core-setup), and set up a `profiles.yml` file.
 
-With a few exceptions [^1], you can install all [Verified adapters](/docs/supported-data-platforms) from PyPI using `pip install adapter-name`. For example to install Snowflake, use the command `pip install dbt-snowflake`. The installation will include `dbt-core` and any other required dependencies, which may include both other dependencies and even other adapter plugins. Read more about [installing dbt](/docs/core/installation).
+With a few exceptions [^1], you can install all [Verified adapters](/docs/supported-data-platforms) from PyPI using `python -m pip install adapter-name`. For example to install Snowflake, use the command `python -m pip install dbt-snowflake`. The installation will include `dbt-core` and any other required dependencies, which may include both other dependencies and even other adapter plugins. Read more about [installing dbt](/docs/core/installation).
 
 [^1]: Here are the two different adapters. Use the PyPI package name when installing with `pip`
 

website/docs/docs/core/connect-data-platform/athena-setup.md

@@ -32,7 +32,7 @@ meta:
 
 pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}</code>
 
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>
 

website/docs/docs/core/connect-data-platform/azuresynapse-setup.md

@@ -41,7 +41,7 @@ Refer to [Microsoft Fabric Synapse Data Warehouse](/docs/core/connect-data-platf
 
 pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}</code>
 
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>
 

website/docs/docs/core/connect-data-platform/bigquery-setup.md

@@ -35,7 +35,7 @@ meta:
 
 pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}</code>
 
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>
 

website/docs/docs/core/connect-data-platform/clickhouse-setup.md

@@ -36,7 +36,7 @@ Some core functionality may be limited. If you're interested in contributing, ch
 
 pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}</code>
 
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>
 

website/docs/docs/core/connect-data-platform/databend-setup.md

@@ -40,7 +40,7 @@ If you're interested in contributing, check out the source code repository liste
 
 pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}</code>
 
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>
 

website/docs/docs/core/connect-data-platform/databricks-setup.md

@@ -36,7 +36,7 @@ meta:
 
 pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}</code>
 
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>
 

website/docs/docs/core/connect-data-platform/decodable-setup.md

@@ -39,7 +39,7 @@ Some core functionality may be limited. If you're interested in contributing, se
 
 dbt-decodable is also available on PyPI. pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}</code>
 
 <br/>
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>

website/docs/docs/core/connect-data-platform/doris-setup.md

@@ -33,7 +33,7 @@ meta:
 
 pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}</code>
 
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>
 

website/docs/docs/core/connect-data-platform/dremio-setup.md

@@ -39,7 +39,7 @@ Some core functionality may be limited. If you're interested in contributing, ch
 
 pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}</code>
 
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>
 

website/docs/docs/core/connect-data-platform/duckdb-setup.md

@@ -39,7 +39,7 @@ Some core functionality may be limited. If you're interested in contributing, ch
 
 pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}</code>
 
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>
 

website/docs/docs/core/connect-data-platform/exasol-setup.md

@@ -39,7 +39,7 @@ Some core functionality may be limited. If you're interested in contributing, ch
 
 pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}</code>
 
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>
 

website/docs/docs/core/connect-data-platform/fabric-setup.md

@@ -37,7 +37,7 @@ To learn how to set up dbt with Azure Synapse Dedicated Pools, see [Microsoft Az
 
 pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}</code>
 
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>
 

website/docs/docs/core/connect-data-platform/fal-setup.md

@@ -39,7 +39,7 @@ Some core functionality may be limited. If you're interested in contributing, ch
 
 pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}[&lt;sql-adapter&gt;]</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}[&lt;sql-adapter&gt;]</code>
 
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>
 

website/docs/docs/core/connect-data-platform/firebolt-setup.md

@@ -37,7 +37,7 @@ Some core functionality may be limited. If you're interested in contributing, ch
 
 pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}</code>
 
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>
 

website/docs/docs/core/connect-data-platform/glue-setup.md

@@ -40,7 +40,7 @@ Some core functionality may be limited. If you're interested in contributing, ch
 
 pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}</code>
 
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>
 
@@ -210,7 +210,7 @@ Configure a Python virtual environment to isolate package version and code depen
 $ sudo yum install git
 $ python3 -m venv dbt_venv
 $ source dbt_venv/bin/activate
-$ python3 -m pip install --upgrade pip
+$ python3 -m python -m pip install --upgrade pip
 ```
 
 Configure the last version of AWS CLI

website/docs/docs/core/connect-data-platform/greenplum-setup.md

@@ -34,7 +34,7 @@ meta:
 
 pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}</code>
 
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>
 

website/docs/docs/core/connect-data-platform/hive-setup.md

@@ -34,7 +34,7 @@ meta:
 
 pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}</code>
 
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>
 
@@ -154,7 +154,7 @@ you must install the `dbt-hive` plugin.
 The following commands will install the latest version of `dbt-hive` as well as the requisite version of `dbt-core` and `impyla` driver used for connections.
 
 ```
-pip install dbt-hive
+python -m pip install dbt-hive
 ```
 
 ### Supported Functionality

website/docs/docs/core/connect-data-platform/ibmdb2-setup.md

@@ -40,7 +40,7 @@ Some core functionality may be limited. If you're interested in contributing, ch
 
 pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}</code>
 
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>
 

website/docs/docs/core/connect-data-platform/impala-setup.md

@@ -34,7 +34,7 @@ meta:
 
 pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}</code>
 
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>
 

website/docs/docs/core/connect-data-platform/infer-setup.md

@@ -34,7 +34,7 @@ meta:
 
 pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}</code>
 
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>
 

website/docs/docs/core/connect-data-platform/iomete-setup.md

@@ -36,7 +36,7 @@ meta:
 
 pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}</code>
 
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>
 

website/docs/docs/core/connect-data-platform/layer-setup.md

@@ -36,7 +36,7 @@ meta:
 
 pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}</code>
 
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>
 

website/docs/docs/core/connect-data-platform/materialize-setup.md

@@ -39,7 +39,7 @@ Certain core functionality may vary. If you would like to report a bug, request
 
 pip is the easiest way to install the adapter:
 
-<code>pip install {frontMatter.meta.pypi_package}</code>
+<code>python -m pip install {frontMatter.meta.pypi_package}</code>
 
 <p>Installing <code>{frontMatter.meta.pypi_package}</code> will also install <code>dbt-core</code> and any other dependencies.</p>