Updated installation command in destination docs and a few others

docs/website/docs/dlt-ecosystem/destinations/athena.md

@@ -11,7 +11,7 @@ The Athena destination stores data as Parquet files in S3 buckets and creates [e
 ## Install dlt with Athena
 **To install the dlt library with Athena dependencies:**
 ```sh
-pip install dlt[athena]
+pip install "dlt[athena]"
 ```
 
 ## Setup Guide
@@ -30,7 +30,7 @@ First, install dependencies by running:
 ```sh
 pip install -r requirements.txt
 ```
-or with `pip install dlt[athena]`, which will install `s3fs`, `pyarrow`, `pyathena`, and `botocore` packages.
+or with `pip install "dlt[athena]"`, which will install `s3fs`, `pyarrow`, `pyathena`, and `botocore` packages.
 
 :::caution
 

docs/website/docs/dlt-ecosystem/destinations/bigquery.md

@@ -11,7 +11,7 @@ keywords: [bigquery, destination, data warehouse]
 **To install the dlt library with BigQuery dependencies:**
 
 ```sh
-pip install dlt[bigquery]
+pip install "dlt[bigquery]"
 ```
 
 ## Setup Guide

docs/website/docs/dlt-ecosystem/destinations/clickhouse.md

@@ -11,7 +11,7 @@ keywords: [ clickhouse, destination, data warehouse ]
 **To install the DLT library with ClickHouse dependencies:**
 
 ```sh
-pip install dlt[clickhouse]
+pip install "dlt[clickhouse]"
 ```
 
 ## Setup Guide
@@ -33,7 +33,7 @@ requirements file by executing it as follows:
 pip install -r requirements.txt
 ```
 
-or with `pip install dlt[clickhouse]`, which installs the `dlt` library and the necessary dependencies for working with ClickHouse as a destination.
+or with `pip install "dlt[clickhouse]"`, which installs the `dlt` library and the necessary dependencies for working with ClickHouse as a destination.
 
 ### 2. Setup ClickHouse database
 

docs/website/docs/dlt-ecosystem/destinations/databricks.md

@@ -12,7 +12,7 @@ keywords: [Databricks, destination, data warehouse]
 ## Install dlt with Databricks
 **To install the dlt library with Databricks dependencies:**
 ```sh
-pip install dlt[databricks]
+pip install "dlt[databricks]"
 ```
 
 ## Set up your Databricks workspace

docs/website/docs/dlt-ecosystem/destinations/dremio.md

@@ -9,7 +9,7 @@ keywords: [dremio, iceberg, aws, glue catalog]
 ## Install dlt with Dremio
 **To install the dlt library with Dremio and s3 dependencies:**
 ```sh
-pip install dlt[dremio,s3]
+pip install "dlt[dremio,s3]"
 ```
 
 ## Setup Guide
@@ -28,7 +28,7 @@ First install dependencies by running:
 ```sh
 pip install -r requirements.txt
 ```
-or with `pip install dlt[dremio,s3]` which will install `s3fs`, `pyarrow`, and `botocore` packages.
+or with `pip install "dlt[dremio,s3]"` which will install `s3fs`, `pyarrow`, and `botocore` packages.
 
 To edit the `dlt` credentials file with your secret info, open `.dlt/secrets.toml`. You will need to provide a `bucket_url` which holds the uploaded parquet files.
 

docs/website/docs/dlt-ecosystem/destinations/duckdb.md

@@ -9,7 +9,7 @@ keywords: [duckdb, destination, data warehouse]
 ## Install dlt with DuckDB
 **To install the dlt library with DuckDB dependencies, run:**
 ```sh
-pip install dlt[duckdb]
+pip install "dlt[duckdb]"
 ```
 
 ## Setup Guide

docs/website/docs/dlt-ecosystem/destinations/filesystem.md

@@ -6,7 +6,7 @@ The Filesystem destination stores data in remote file systems and bucket storage
 ## Install dlt with filesystem
 **To install the dlt library with filesystem dependencies:**
 ```sh
-pip install dlt[filesystem]
+pip install "dlt[filesystem]"
 ```
 
 This installs `s3fs` and `botocore` packages.
@@ -125,7 +125,7 @@ client_kwargs = '{"verify": "public.crt"}'
 ```
 
 #### Google Storage
-Run `pip install dlt[gs]` which will install the `gcfs` package.
+Run `pip install "dlt[gs]"` which will install the `gcfs` package.
 
 To edit the `dlt` credentials file with your secret info, open `.dlt/secrets.toml`.
 You'll see AWS credentials by default.
@@ -148,7 +148,7 @@ if you have default google cloud credentials in your environment (i.e. on cloud
 Use **Cloud Storage** admin to create a new bucket. Then assign the **Storage Object Admin** role to your service account.
 
 #### Azure Blob Storage
-Run `pip install dlt[az]` which will install the `adlfs` package to interface with Azure Blob Storage.
+Run `pip install "dlt[az]"` which will install the `adlfs` package to interface with Azure Blob Storage.
 
 Edit the credentials in `.dlt/secrets.toml`, you'll see AWS credentials by default replace them with your Azure credentials:
 

docs/website/docs/dlt-ecosystem/destinations/motherduck.md

@@ -10,7 +10,7 @@ keywords: [MotherDuck, duckdb, destination, data warehouse]
 ## Install dlt with MotherDuck
 **To install the dlt library with MotherDuck dependencies:**
 ```sh
-pip install dlt[motherduck]
+pip install "dlt[motherduck]"
 ```
 
 :::tip

docs/website/docs/dlt-ecosystem/destinations/mssql.md

@@ -9,7 +9,7 @@ keywords: [mssql, sqlserver, destination, data warehouse]
 ## Install dlt with MS SQL
 **To install the dlt library with MS SQL dependencies, use:**
 ```sh
-pip install dlt[mssql]
+pip install "dlt[mssql]"
 ```
 
 ## Setup guide
@@ -38,7 +38,7 @@ pip install -r requirements.txt
 ```
 or run:
 ```sh
-pip install dlt[mssql]
+pip install "dlt[mssql]"
 ```
 This will install `dlt` with the `mssql` extra, which contains all the dependencies required by the SQL server client.
 

docs/website/docs/dlt-ecosystem/destinations/postgres.md

@@ -9,7 +9,7 @@ keywords: [postgres, destination, data warehouse]
 ## Install dlt with PostgreSQL
 **To install the dlt library with PostgreSQL dependencies, run:**
 ```sh
-pip install dlt[postgres]
+pip install "dlt[postgres]"
 ```
 
 ## Setup Guide

docs/website/docs/dlt-ecosystem/destinations/qdrant.md

@@ -14,7 +14,7 @@ This destination helps you load data into Qdrant from [dlt resources](../../gene
 1. To use Qdrant as a destination, make sure `dlt` is installed with the `qdrant` extra:
 
 ```sh
-pip install dlt[qdrant]
+pip install "dlt[qdrant]"
 ```
 
 2. Next, configure the destination in the dlt secrets file. The file is located at `~/.dlt/secrets.toml` by default. Add the following section to the secrets file:

docs/website/docs/dlt-ecosystem/destinations/redshift.md

@@ -9,7 +9,7 @@ keywords: [redshift, destination, data warehouse]
 ## Install dlt with Redshift
 **To install the dlt library with Redshift dependencies:**
 ```sh
-pip install dlt[redshift]
+pip install "dlt[redshift]"
 ```
 
 ## Setup Guide
@@ -26,7 +26,7 @@ The above command generates several files and directories, including `.dlt/secre
 ```sh
 pip install -r requirements.txt
 ```
-or with `pip install dlt[redshift]`, which installs the `dlt` library and the necessary dependencies for working with Amazon Redshift as a destination.
+or with `pip install "dlt[redshift]"`, which installs the `dlt` library and the necessary dependencies for working with Amazon Redshift as a destination.
 
 ### 2. Setup Redshift cluster
 To load data into Redshift, you need to create a Redshift cluster and enable access to your IP address through the VPC inbound rules associated with the cluster. While we recommend asking our GPT-4 assistant for details, we have provided a general outline of the process below:

docs/website/docs/dlt-ecosystem/destinations/synapse.md

@@ -9,7 +9,7 @@ keywords: [synapse, destination, data warehouse]
 ## Install dlt with Synapse
 **To install the dlt library with Synapse dependencies:**
 ```sh
-pip install dlt[synapse]
+pip install "dlt[synapse]"
 ```
 
 ## Setup guide

docs/website/docs/dlt-ecosystem/destinations/weaviate.md

@@ -14,7 +14,7 @@ This destination helps you load data into Weaviate from [dlt resources](../../ge
 1. To use Weaviate as a destination, make sure dlt is installed with the 'weaviate' extra:
 
 ```sh
-pip install dlt[weaviate]
+pip install "dlt[weaviate]"
 ```
 
 2. Next, configure the destination in the dlt secrets file. The file is located at `~/.dlt/secrets.toml` by default. Add the following section to the secrets file:

docs/website/docs/dlt-ecosystem/file-formats/parquet.md

@@ -11,7 +11,7 @@ keywords: [parquet, file formats]
 To use this format, you need a `pyarrow` package. You can get this package as a `dlt` extra as well:
 
 ```sh
-pip install dlt[parquet]
+pip install "dlt[parquet]"
 ```
 
 ## Supported Destinations

docs/website/docs/general-usage/data-enrichments/currency_conversion_data_enrichment.md

@@ -247,7 +247,7 @@ API token.
    [destination](../../dlt-ecosystem/destinations/), For example, duckdb:
 
    ```sh
-   pip install dlt[duckdb]
+   pip install "dlt[duckdb]"
    ```
 
 1. Run the pipeline with the following command:

docs/website/docs/general-usage/data-enrichments/url-parser-data-enrichment.md

@@ -231,7 +231,7 @@ need to register to use this service neither get an API key.
    [destination](https://dlthub.com/docs/dlt-ecosystem/destinations/), For example, duckdb:
 
    ```sh
-   pip install dlt[duckdb]
+   pip install "dlt[duckdb]"
    ```
 
 1. Run the pipeline with the following command:

docs/website/docs/general-usage/data-enrichments/user_agent_device_data_enrichment.md

@@ -284,7 +284,7 @@ The first step is to register on [SerpAPI](https://serpapi.com/) and obtain the
    [destination](https://dlthub.com/docs/dlt-ecosystem/destinations/), For example, duckdb:
 
    ```sh
-   pip install dlt[duckdb]
+   pip install "dlt[duckdb]"
    ```
 
 1. Run the pipeline with the following command: