guide: remove Basic Concepts page

per #1581 (review)

content/docs/sidebar.json

@@ -87,7 +87,6 @@
         "slug": "what-is-dvc",
         "source": "what-is-dvc.md"
       },
-      "basic-concepts",
       {
         "label": "DVC Files and Directories",
         "slug": "dvc-files-and-directories"

content/docs/user-guide/related-technologies.md

@@ -6,11 +6,10 @@ bringing best practices from software engineering into the data science field
 
 ## Git
 
-- DVC builds upon Git by introducing the concept of
-  [data files](/doc/user-guide/basic-concepts#data-files) – large files that
-  should not be stored in a Git repository, but still need to be tracked and
-  versioned. It leverages Git's features to enable managing different versions
-  of data itself, data pipelines, and experiments.
+- DVC builds upon Git by introducing the concept of data files – large files
+  that should not be stored in a Git repository, but still need to be tracked
+  and versioned. It leverages Git's features to enable managing different
+  versions of data itself, data pipelines, and experiments.
 
 - DVC is not fundamentally bound to Git, and can work without it (except
   versioning-related features). This also applies to Git-LFS and Git-annex,
@@ -27,7 +26,7 @@ bringing best practices from software engineering into the data science field
   [available](/doc/command-reference/install)).
 
 - Git-LFS was not made with data science in mind, so it doesn't provide related
-  features (e.g. [pipelines](/doc/user-guide/basic-concepts#data-pipeline),
+  features (e.g. [pipelines](/doc/command-reference/pipeline),
   [metrics](/doc/command-reference/metrics), etc.).
 
 - Github (most common Git hosting service) has a limit of 2 GB per repository.
@@ -116,14 +115,13 @@ _Luigi_, etc.
   (DAG):
 
   - The DAG or dependency graph is defined implicitly by the connections between
-    pipeline [stages](/doc/user-guide/basic-concepts#data-processing-stage),
-    based on their <abbr>dependencies</abbr> and <abbr>outputs</abbr>.
+    pipeline [stages](/doc/command-reference/run), based on their
+    <abbr>dependencies</abbr> and <abbr>outputs</abbr>.
 
   - Each stage defines one node in the DAG. All DVC-files in a repository make
-    up a [pipelines](/doc/user-guide/basic-concepts#data-pipeline) (think a
-    single Makefile). All stages (and corresponding processes) are implicitly
-    combined through their inputs and outputs, simplifying conflict resolution
-    during merges.
+    up a [pipelines](/doc/command-reference/pipeline) (think a single Makefile).
+    All stages (and corresponding processes) are implicitly combined through
+    their inputs and outputs, simplifying conflict resolution during merges.
 
   - DVC stages can be written manually in an intuitive `dvc.yaml` file, or
     generated by the helper command `dvc run`, based on a terminal command, its

content/docs/user-guide/what-is-dvc.md

@@ -21,8 +21,7 @@ software engineers.
   interface and flow as Git. DVC can also work stand-alone, but without
   versioning capabilities.
 
-- **Data versioning** is enabled by replacing
-  [large files](/doc/user-guide/basic-concepts#data-files), dataset directories,
+- **Data versioning** is enabled by replacing large files, dataset directories,
   ML models, etc. with small
   [metafiles](/doc/user-guide/dvc-files-and-directories) (easy to handle with
   Git). These placeholders point to the original data, which is decoupled from
@@ -33,8 +32,8 @@ software engineers.
   transfer large datasets or share a GPU-trained model with others.
 
 - DVC makes data science projects **reproducible** by creating lightweight
-  [pipelines](/doc/user-guide/basic-concepts#data-pipelines) using implicit
-  dependency graphs,and codifying the data and artifacts involved.
+  [pipelines](/doc/command-reference/pipeline) using implicit dependency
+  graphs,and codifying the data and artifacts involved.
 
 - DVC is **platform agnostic**: It runs on all major operating systems (Linux,
   MacOS, and Windows), and works independently of the programming languages