diff --git a/content/blog/2024-sql-notebooks-with-quarto/index.md b/content/blog/2024-sql-notebooks-with-quarto/index.md
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 ---
 
 
-Notebooks are a cornerstone of exploratory data analysis in data science (and sometimes also [in production](https://ploomber.io/blog/nbs-production/) for some godforgiven reason?). Notebooks get a fair share of (justified) [criticism](https://yobibyte.github.io/notebooks.html), but I believe they are useful if used right, particularly in the education sphere where you can get immediate feedback on the code you wrote. The best-known notebook platforms are [Jupyter](https://jupyter.org) for Python (although it can actually [R](https://docs.anaconda.com/navigator/tutorials/r-lang/https://docs.anaconda.com/navigator/tutorials/r-lang/) and [Julia](https://www.jousefmurad.com/coding/install-julia-jupyter-notebook/) too) and [R Markdown](https://rmarkdown.rstudio.com). However, since 2022 a natural successor to R Markdown was released called [Quarto](https://quarto.org) that natively supports Python, R, and Julia. I've become quite an advocate for Quarto since it was released since it works well for both Python and R out-of-the-box without any extensions necessary. It's also Git compatible, which is one of the other major drawbacks from Jupyter in my opinion. One of the main languages that currently does not have a widely used notebook platform is SQL. Although some languages and companies have made attempts. For example Snowflake and SQL Server have a (pseudo)notebook support in their IDEs and Meta uses an [internal tool](https://engineering.fb.com/2022/04/26/developer-tools/sql-notebooks/) that supports analyses in SQL notebooks. That's why I want to show you today how you can turn Quarto into a notebook for SQL analyses that can be rendered into webpages, PDFs, markdown documents, or online books and documentation.
+Notebooks are a cornerstone of exploratory data analysis in data science (and sometimes also [in production](https://ploomber.io/blog/nbs-production/) for some godforgiven reason?). Notebooks get a fair share of (justified) [criticism](https://yobibyte.github.io/notebooks.html), but I believe they are useful if used right, particularly in the education sphere where you can get immediate feedback on the code you wrote. The best-known notebook platforms are [Jupyter](https://jupyter.org) for Python (although it can actually [R](https://docs.anaconda.com/navigator/tutorials/r-lang/https://docs.anaconda.com/navigator/tutorials/r-lang/) and [Julia](https://www.jousefmurad.com/coding/install-julia-jupyter-notebook/) too) and [R Markdown](https://rmarkdown.rstudio.com). However, since 2022 a natural successor to R Markdown was released called [Quarto](https://quarto.org) that natively supports Python, R, and Julia. I've become quite an advocate for Quarto since it was released since it works well for both Python and R out-of-the-box without any extensions necessary. It's also Git compatible, which is one of the other major drawbacks from Jupyter in my opinion. One of the main languages that currently does not have a widely used notebook platform is SQL. Although some languages and companies have made attempts. For example [Snowflake](https://www.snowflake.com/blog/introducing-snowflake-notebooks/) and [SQL Server](https://learn.microsoft.com/en-us/azure-data-studio/notebooks/notebooks-sql-kernel) have a (pseudo)notebook support in their IDEs and Meta uses an [internal tool](https://engineering.fb.com/2022/04/26/developer-tools/sql-notebooks/) that supports analyses in SQL notebooks. That's why I want to show you today how you can turn Quarto into a notebook for SQL analyses that can be rendered into webpages, PDFs, markdown documents, or online books and documentation.
 
 ## Background
 
diff --git a/content/blog/2024-sql-notebooks-with-quarto/index.qmd b/content/blog/2024-sql-notebooks-with-quarto/index.qmd
index 9cb25d7..8dbef56 100644
--- a/content/blog/2024-sql-notebooks-with-quarto/index.qmd
+++ b/content/blog/2024-sql-notebooks-with-quarto/index.qmd
@@ -14,7 +14,7 @@ editor_options:
   chunk_output_type: console
 ---
 
-Notebooks are a cornerstone of exploratory data analysis in data science (and sometimes also [in production](https://ploomber.io/blog/nbs-production/) for some godforgiven reason?). Notebooks get a fair share of (justified) [criticism](https://yobibyte.github.io/notebooks.html), but I believe they are useful if used right, particularly in the education sphere where you can get immediate feedback on the code you wrote. The best-known notebook platforms are [Jupyter](https://jupyter.org) for Python (although it can actually [R](https://docs.anaconda.com/navigator/tutorials/r-lang/https://docs.anaconda.com/navigator/tutorials/r-lang/) and [Julia](https://www.jousefmurad.com/coding/install-julia-jupyter-notebook/) too) and [R Markdown](https://rmarkdown.rstudio.com). However, since 2022 a natural successor to R Markdown was released called [Quarto](https://quarto.org) that natively supports Python, R, and Julia. I've become quite an advocate for Quarto since it was released since it works well for both Python and R out-of-the-box without any extensions necessary. It's also Git compatible, which is one of the other major drawbacks from Jupyter in my opinion. One of the main languages that currently does not have a widely used notebook platform is SQL. Although some languages and companies have made attempts. For example Snowflake and SQL Server have a (pseudo)notebook support in their IDEs and Meta uses an [internal tool](https://engineering.fb.com/2022/04/26/developer-tools/sql-notebooks/) that supports analyses in SQL notebooks. That's why I want to show you today how you can turn Quarto into a notebook for SQL analyses that can be rendered into webpages, PDFs, markdown documents, or online books and documentation.
+Notebooks are a cornerstone of exploratory data analysis in data science (and sometimes also [in production](https://ploomber.io/blog/nbs-production/) for some godforgiven reason?). Notebooks get a fair share of (justified) [criticism](https://yobibyte.github.io/notebooks.html), but I believe they are useful if used right, particularly in the education sphere where you can get immediate feedback on the code you wrote. The best-known notebook platforms are [Jupyter](https://jupyter.org) for Python (although it can actually [R](https://docs.anaconda.com/navigator/tutorials/r-lang/https://docs.anaconda.com/navigator/tutorials/r-lang/) and [Julia](https://www.jousefmurad.com/coding/install-julia-jupyter-notebook/) too) and [R Markdown](https://rmarkdown.rstudio.com). However, since 2022 a natural successor to R Markdown was released called [Quarto](https://quarto.org) that natively supports Python, R, and Julia. I've become quite an advocate for Quarto since it was released since it works well for both Python and R out-of-the-box without any extensions necessary. It's also Git compatible, which is one of the other major drawbacks from Jupyter in my opinion. One of the main languages that currently does not have a widely used notebook platform is SQL. Although some languages and companies have made attempts. For example [Snowflake](https://www.snowflake.com/blog/introducing-snowflake-notebooks/) and [SQL Server](https://learn.microsoft.com/en-us/azure-data-studio/notebooks/notebooks-sql-kernel) have a (pseudo)notebook support in their IDEs and Meta uses an [internal tool](https://engineering.fb.com/2022/04/26/developer-tools/sql-notebooks/) that supports analyses in SQL notebooks. That's why I want to show you today how you can turn Quarto into a notebook for SQL analyses that can be rendered into webpages, PDFs, markdown documents, or online books and documentation.
 
 ## Background