Merge pull request #891 from keboola/devin/1773996726-cleanup-julia-references

docs: remove references to Julia transformations and workspaces

Author: natocTo

catalog/multi-project/index.md

@@ -78,7 +78,7 @@ come to the company, they can simply create a new project, use the shared data w
 working immediately. If the word "everyone" in the above statement is undesired, then the access can be tied down by using a different
 [sharing type](/catalog/#sharing-types). The multi architecture setup also creates an interface. If the Oracle DBAs need to change 
 anything in the database schema, they can do that in an unlimited number of arbitrarily sized steps, provided that the shared data 
-remains same. For that they can use the entire power of SQL, Python, R and Julia transformations combined. This is a much more flexible 
+remains same. For that they can use the entire power of SQL, Python, and R transformations combined. This is a much more flexible 
 solution then the traditional way of creating views. Then there is, of course, the advantage that you can work with the Oracle data 
 even when the server is down; but that's integral part of Keboola.
 

overview/index.md

@@ -53,14 +53,14 @@ making it easier to connect with other parts of the system and third-party appli
 
 ### Transformations & Workspaces
 [Transformations](/transformations/) allow you to manipulate data in your project. They are the tasks you want to perform and enable you to write custom scripts
-in [SQL](https://en.wikipedia.org/wiki/SQL) (Snowflake, Redshift, BigQuery, etc.), dbt, [Julia](https://julialang.org/), [Python](https://www.python.org/about/), 
+in [SQL](https://en.wikipedia.org/wiki/SQL) (Snowflake, Redshift, BigQuery, etc.), dbt, [Python](https://www.python.org/about/), 
 and [R](https://www.r-project.org/about.html). 
 
 All transformations operate on a copy of Storage data in an isolated environment — a [workspace](/workspace/), guaranteeing safety for your
 analyses and experimentation. Workspaces support collaborations and can be shared. 
 
 - **SQL workspaces** are accessible through the database provider's IDE or your preferred SQL IDE.
-- **Python, R, or Julia workspaces** are available through Keboola's hosted and managed Jupyter Lab environment.
+- **Python and R workspaces** are available through Keboola's hosted and managed JupyterLab environment.
 
 ### Applications
 Unlike free-form transformations, [applications](/components/applications/) are **predefined blocks** that enable users to perform advanced tasks such as 

transformations/index.md

@@ -30,7 +30,7 @@ The schema below shows a high-level overview of transformations:
 {: .image-popup}
 ![Transformations schema](/transformations/transformations-schema.svg)
 
-A transformation is represented by a **transformation script** (SQL, Julia, Python, R), which you
+A transformation is represented by a **transformation script** (SQL, Python, R), which you
 can use to manipulate your data. To ensure the safety of the data in Storage, a transformation
 operates in a separate **staging Storage** created for each transformation. 
 
@@ -69,9 +69,8 @@ A backend is the engine running the transformation script. It is a database serv
 [Microsoft Synapse](https://azure.microsoft.com/en-us/services/synapse-analytics/) on Azure Stack), 
 [BigQuery](https://cloud.google.com/bigquery), 
 or a language interpreter 
-([Julia](https://julialang.org/), 
-[Python](https://www.python.org/about/),
-[R](https://www.r-project.org/about.html).
+([Python](https://www.python.org/about/),
+[R](https://www.r-project.org/about.html)).
 
 How do you decide **which backend is appropriate for each task**? A rule of thumb is that SQL performs better
 for joining tables, filtering data, grouping, and simple aggregations. Script languages are more suitable
@@ -107,8 +106,8 @@ use with a database client of your choice. You can do the same for Snowflake. In
 to the [Snowflake web interface](https://docs.snowflake.com/en/user-guide/ui-snowsight-gs). Therefore, you can
 develop transformations without downloading and installing a database client.
 
-You can also use a workspace represented by an isolated [JupyterLab](https://jupyterlab.readthedocs.io/en/latest/) instance for Julia, 
-Python, and R transformations.
+You can also use a workspace represented by an isolated [JupyterLab](https://jupyterlab.readthedocs.io/en/latest/) instance for
+Python and R transformations.
 
 ## Transformations Features
 
@@ -118,11 +117,7 @@ Python, and R transformations.
     <th>Availability/Limitations</th>
 </tr>
 <tr>
-    <th rowspan='8'>Backend</th>
-    <th>Julia Transformations</th> 
-    <td>Planned</td>
-</tr>
-<tr>
+    <th rowspan='7'>Backend</th>
     <th><a href='/transformations/python-plain/'>Python Transformations</a></th> 
     <td>✓</td>
 </tr>
@@ -276,4 +271,4 @@ When triggered
 
 With the [read-only input mapping](/transformations/mappings/#read-only-input-mapping) feature, you can access all buckets (your own or linked) in transformations. Your transformation user
 has read-only access to buckets (and their tables), so you can access such data. So, there is no need to specify standard input mapping 
-for your transformations. The name of the backend object (database, schema, etc.) depends on the backend you use, and it contains the bucket ID (not the bucket name). 
+for your transformations. The name of the backend object (database, schema, etc.) depends on the backend you use, and it contains the bucket ID (not the bucket name).    

tutorial/ad-hoc/index.md

@@ -11,7 +11,7 @@ features related to doing ad-hoc analysis.
 
 This part of the tutorial shows how to work with arbitrary data in Python
 in a completely unrestricted way. Although our examples use the Python language,
-the very same can be achieved using R or Julia.
+the very same can be achieved using R.
 
 Before you start, you should have a basic understanding of the [Python language](https://www.python.org/).
 
@@ -277,14 +277,13 @@ simply copy&paste it into a [Transformation](/tutorial/manipulate/).
 
 The above tutorial is done in the [Python language](https://www.python.org/) using the
 [Jupyter Notebook](https://jupyter.org/). The same can be done in the
-[R language](https://www.r-project.org/) using [RStudio](https://rstudio.com/),
-or in [Julia](https://julialang.org/) using [Jupyter Notebook](https://jupyter.org/).
+[R language](https://www.r-project.org/) using [RStudio](https://rstudio.com/).
 For more information about workspaces (including disk and memory limits), see the
 [corresponding documentation](/workspace/).
 
 ## Final Note
 This is the end of our stroll around Keboola. On our walk, we missed quite a few things:
-Applications, Python, R and Julia transformations, Redshift and Snowflake features, to name a few.
+Applications, Python and R transformations, Redshift and Snowflake features, to name a few.
 However, teaching you everything was not really the point of this tutorial.
 We wanted to show you how Keboola can help in connecting different systems together.