diff --git a/docs/add_plugin_git.png b/docs/add_plugin_git.png
new file mode 100644
index 0000000..1e59cca
Binary files /dev/null and b/docs/add_plugin_git.png differ
diff --git a/docs/backend/404.html b/docs/backend/404.html
index 9b161ba..587afd7 100644
--- a/docs/backend/404.html
+++ b/docs/backend/404.html
@@ -15,7 +15,7 @@
     
     
       
-        <title>Data Editing Backend</title>
+        <title>Visual Edit Backend</title>
       
     
     
@@ -70,7 +70,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="/." title="Data Editing Backend" class="md-header__button md-logo" aria-label="Data Editing Backend" data-md-component="logo">
+    <a href="/." title="Visual Edit Backend" class="md-header__button md-logo" aria-label="Visual Edit Backend" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54Z"/></svg>
@@ -83,7 +83,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            Data Editing Backend
+            Visual Edit Backend
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -156,13 +156,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="/." title="Data Editing Backend" class="md-nav__button md-logo" aria-label="Data Editing Backend" data-md-component="logo">
+    <a href="/." title="Visual Edit Backend" class="md-nav__button md-logo" aria-label="Visual Edit Backend" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54Z"/></svg>
 
     </a>
-    Data Editing Backend
+    Visual Edit Backend
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
diff --git a/docs/backend/index.html b/docs/backend/index.html
index f50589b..6f26e13 100644
--- a/docs/backend/index.html
+++ b/docs/backend/index.html
@@ -15,7 +15,7 @@
     
     
       
-        <title>Data Editing Backend</title>
+        <title>Visual Edit Backend</title>
       
     
     
@@ -59,7 +59,7 @@
     <div data-md-component="skip">
       
         
-        <a href="#EditableEventSourced" class="md-skip">
+        <a href="#DataEditor" class="md-skip">
           Skip to content
         </a>
       
@@ -75,7 +75,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="." title="Data Editing Backend" class="md-header__button md-logo" aria-label="Data Editing Backend" data-md-component="logo">
+    <a href="." title="Visual Edit Backend" class="md-header__button md-logo" aria-label="Visual Edit Backend" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54Z"/></svg>
@@ -88,7 +88,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            Data Editing Backend
+            Visual Edit Backend
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -161,13 +161,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="." title="Data Editing Backend" class="md-nav__button md-logo" aria-label="Data Editing Backend" data-md-component="logo">
+    <a href="." title="Visual Edit Backend" class="md-nav__button md-logo" aria-label="Visual Edit Backend" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54Z"/></svg>
 
     </a>
-    Data Editing Backend
+    Visual Edit Backend
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -210,92 +210,92 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#EditableEventSourced" class="md-nav__link">
-    EditableEventSourced
+  <a href="#DataEditor" class="md-nav__link">
+    DataEditor
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced" class="md-nav__link">
-    EditableEventSourced
+  <a href="#DataEditor.DataEditor" class="md-nav__link">
+    DataEditor
   </a>
   
-    <nav class="md-nav" aria-label="EditableEventSourced">
+    <nav class="md-nav" aria-label="DataEditor">
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced.create_row" class="md-nav__link">
+  <a href="#DataEditor.DataEditor.create_row" class="md-nav__link">
     create_row()
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced.delete_row" class="md-nav__link">
+  <a href="#DataEditor.DataEditor.delete_row" class="md-nav__link">
     delete_row()
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced.empty_editlog" class="md-nav__link">
+  <a href="#DataEditor.DataEditor.empty_editlog" class="md-nav__link">
     empty_editlog()
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced.get_edited_cells_df" class="md-nav__link">
+  <a href="#DataEditor.DataEditor.get_edited_cells_df" class="md-nav__link">
     get_edited_cells_df()
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced.get_edited_cells_df_indexed" class="md-nav__link">
+  <a href="#DataEditor.DataEditor.get_edited_cells_df_indexed" class="md-nav__link">
     get_edited_cells_df_indexed()
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced.get_edited_df" class="md-nav__link">
+  <a href="#DataEditor.DataEditor.get_edited_df" class="md-nav__link">
     get_edited_df()
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced.get_edited_df_indexed" class="md-nav__link">
+  <a href="#DataEditor.DataEditor.get_edited_df_indexed" class="md-nav__link">
     get_edited_df_indexed()
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced.get_editlog_df" class="md-nav__link">
+  <a href="#DataEditor.DataEditor.get_editlog_df" class="md-nav__link">
     get_editlog_df()
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced.get_original_df" class="md-nav__link">
+  <a href="#DataEditor.DataEditor.get_original_df" class="md-nav__link">
     get_original_df()
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced.get_row" class="md-nav__link">
+  <a href="#DataEditor.DataEditor.get_row" class="md-nav__link">
     get_row()
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced.update_row" class="md-nav__link">
+  <a href="#DataEditor.DataEditor.update_row" class="md-nav__link">
     update_row()
   </a>
   
@@ -339,92 +339,92 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#EditableEventSourced" class="md-nav__link">
-    EditableEventSourced
+  <a href="#DataEditor" class="md-nav__link">
+    DataEditor
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced" class="md-nav__link">
-    EditableEventSourced
+  <a href="#DataEditor.DataEditor" class="md-nav__link">
+    DataEditor
   </a>
   
-    <nav class="md-nav" aria-label="EditableEventSourced">
+    <nav class="md-nav" aria-label="DataEditor">
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced.create_row" class="md-nav__link">
+  <a href="#DataEditor.DataEditor.create_row" class="md-nav__link">
     create_row()
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced.delete_row" class="md-nav__link">
+  <a href="#DataEditor.DataEditor.delete_row" class="md-nav__link">
     delete_row()
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced.empty_editlog" class="md-nav__link">
+  <a href="#DataEditor.DataEditor.empty_editlog" class="md-nav__link">
     empty_editlog()
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced.get_edited_cells_df" class="md-nav__link">
+  <a href="#DataEditor.DataEditor.get_edited_cells_df" class="md-nav__link">
     get_edited_cells_df()
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced.get_edited_cells_df_indexed" class="md-nav__link">
+  <a href="#DataEditor.DataEditor.get_edited_cells_df_indexed" class="md-nav__link">
     get_edited_cells_df_indexed()
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced.get_edited_df" class="md-nav__link">
+  <a href="#DataEditor.DataEditor.get_edited_df" class="md-nav__link">
     get_edited_df()
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced.get_edited_df_indexed" class="md-nav__link">
+  <a href="#DataEditor.DataEditor.get_edited_df_indexed" class="md-nav__link">
     get_edited_df_indexed()
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced.get_editlog_df" class="md-nav__link">
+  <a href="#DataEditor.DataEditor.get_editlog_df" class="md-nav__link">
     get_editlog_df()
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced.get_original_df" class="md-nav__link">
+  <a href="#DataEditor.DataEditor.get_original_df" class="md-nav__link">
     get_original_df()
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced.get_row" class="md-nav__link">
+  <a href="#DataEditor.DataEditor.get_row" class="md-nav__link">
     get_row()
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#EditableEventSourced.EditableEventSourced.update_row" class="md-nav__link">
+  <a href="#DataEditor.DataEditor.update_row" class="md-nav__link">
     update_row()
   </a>
   
@@ -452,13 +452,13 @@
 
   <h1>Home</h1>
 
-<p>This is the technical documentation for the backend of the Data Editing plugin. <a href="../">Return to the main documentation</a>.</p>
+<p>This is the technical documentation for the backend of the Visual Edit plugin. <a href="../">Return to the main documentation</a>.</p>
 
 
 <div class="doc doc-object doc-module">
 
 
-<a id="EditableEventSourced"></a>
+<a id="DataEditor"></a>
   <div class="doc doc-contents first">
 
   
@@ -476,8 +476,8 @@ <h1>Home</h1>
 
 
 
-<h2 id="EditableEventSourced.EditableEventSourced" class="doc doc-heading">
-          <code>EditableEventSourced</code>
+<h2 id="DataEditor.DataEditor" class="doc doc-heading">
+          <code>DataEditor</code>
 
 
 </h2>
@@ -506,7 +506,7 @@ <h2 id="EditableEventSourced.EditableEventSourced" class="doc doc-heading">
 
 
 
-<h3 id="EditableEventSourced.EditableEventSourced.create_row" class="doc doc-heading">
+<h3 id="DataEditor.DataEditor.create_row" class="doc doc-heading">
 <code class="highlight language-python"><span class="n">create_row</span><span class="p">(</span><span class="n">primary_keys</span><span class="p">,</span> <span class="n">column_values</span><span class="p">)</span></code>
 
 </h3>
@@ -599,7 +599,7 @@ <h3 id="EditableEventSourced.EditableEventSourced.create_row" class="doc doc-hea
 
 
 
-<h3 id="EditableEventSourced.EditableEventSourced.delete_row" class="doc doc-heading">
+<h3 id="DataEditor.DataEditor.delete_row" class="doc doc-heading">
 <code class="highlight language-python"><span class="n">delete_row</span><span class="p">(</span><span class="n">primary_keys</span><span class="p">)</span></code>
 
 </h3>
@@ -671,7 +671,7 @@ <h3 id="EditableEventSourced.EditableEventSourced.delete_row" class="doc doc-hea
 
 
 
-<h3 id="EditableEventSourced.EditableEventSourced.empty_editlog" class="doc doc-heading">
+<h3 id="DataEditor.DataEditor.empty_editlog" class="doc doc-heading">
 <code class="highlight language-python"><span class="n">empty_editlog</span><span class="p">()</span></code>
 
 </h3>
@@ -690,7 +690,7 @@ <h3 id="EditableEventSourced.EditableEventSourced.empty_editlog" class="doc doc-
 
 
 
-<h3 id="EditableEventSourced.EditableEventSourced.get_edited_cells_df" class="doc doc-heading">
+<h3 id="DataEditor.DataEditor.get_edited_cells_df" class="doc doc-heading">
 <code class="highlight language-python"><span class="n">get_edited_cells_df</span><span class="p">()</span></code>
 
 </h3>
@@ -734,7 +734,7 @@ <h3 id="EditableEventSourced.EditableEventSourced.get_edited_cells_df" class="do
 
 
 
-<h3 id="EditableEventSourced.EditableEventSourced.get_edited_cells_df_indexed" class="doc doc-heading">
+<h3 id="DataEditor.DataEditor.get_edited_cells_df_indexed" class="doc doc-heading">
 <code class="highlight language-python"><span class="n">get_edited_cells_df_indexed</span><span class="p">()</span></code>
 
 </h3>
@@ -778,7 +778,7 @@ <h3 id="EditableEventSourced.EditableEventSourced.get_edited_cells_df_indexed" c
 
 
 
-<h3 id="EditableEventSourced.EditableEventSourced.get_edited_df" class="doc doc-heading">
+<h3 id="DataEditor.DataEditor.get_edited_df" class="doc doc-heading">
 <code class="highlight language-python"><span class="n">get_edited_df</span><span class="p">()</span></code>
 
 </h3>
@@ -821,7 +821,7 @@ <h3 id="EditableEventSourced.EditableEventSourced.get_edited_df" class="doc doc-
 
 
 
-<h3 id="EditableEventSourced.EditableEventSourced.get_edited_df_indexed" class="doc doc-heading">
+<h3 id="DataEditor.DataEditor.get_edited_df_indexed" class="doc doc-heading">
 <code class="highlight language-python"><span class="n">get_edited_df_indexed</span><span class="p">()</span></code>
 
 </h3>
@@ -864,7 +864,7 @@ <h3 id="EditableEventSourced.EditableEventSourced.get_edited_df_indexed" class="
 
 
 
-<h3 id="EditableEventSourced.EditableEventSourced.get_editlog_df" class="doc doc-heading">
+<h3 id="DataEditor.DataEditor.get_editlog_df" class="doc doc-heading">
 <code class="highlight language-python"><span class="n">get_editlog_df</span><span class="p">()</span></code>
 
 </h3>
@@ -907,7 +907,7 @@ <h3 id="EditableEventSourced.EditableEventSourced.get_editlog_df" class="doc doc
 
 
 
-<h3 id="EditableEventSourced.EditableEventSourced.get_original_df" class="doc doc-heading">
+<h3 id="DataEditor.DataEditor.get_original_df" class="doc doc-heading">
 <code class="highlight language-python"><span class="n">get_original_df</span><span class="p">()</span></code>
 
 </h3>
@@ -950,7 +950,7 @@ <h3 id="EditableEventSourced.EditableEventSourced.get_original_df" class="doc do
 
 
 
-<h3 id="EditableEventSourced.EditableEventSourced.get_row" class="doc doc-heading">
+<h3 id="DataEditor.DataEditor.get_row" class="doc doc-heading">
 <code class="highlight language-python"><span class="n">get_row</span><span class="p">(</span><span class="n">primary_keys</span><span class="p">)</span></code>
 
 </h3>
@@ -980,7 +980,7 @@ <h3 id="EditableEventSourced.EditableEventSourced.get_row" class="doc doc-headin
           </td>
           <td>
             <div class="doc-md-description">
-              <p>A dictionary containing values for all primary keys defined in the initial data editing setup. The set of values must be unique. Example:
+              <p>A dictionary containing values for all primary keys defined in the initial Visual Edit setup. The set of values must be unique. Example:
 {
     "key1": "cat",
     "key2": "2022-12-21",
@@ -1027,7 +1027,7 @@ <h3 id="EditableEventSourced.EditableEventSourced.get_row" class="doc doc-headin
 <li>The current implementation loads all edited rows in memory, then filters the rows that match the provided primary key values.</li>
 <li>This method does not read rows that were not edited, and it does not read columns which are not editable.<ul>
 <li>If some rows of the dataset were created, then by definition all columns are editable (including primary keys).</li>
-<li>If no row was created, editable columns are those defined in the initial data editing setup.</li>
+<li>If no row was created, editable columns are those defined in the initial Visual Edit setup.</li>
 </ul>
 </li>
 </ul>
@@ -1041,7 +1041,7 @@ <h3 id="EditableEventSourced.EditableEventSourced.get_row" class="doc doc-headin
 
 
 
-<h3 id="EditableEventSourced.EditableEventSourced.update_row" class="doc doc-heading">
+<h3 id="DataEditor.DataEditor.update_row" class="doc doc-heading">
 <code class="highlight language-python"><span class="n">update_row</span><span class="p">(</span><span class="n">primary_keys</span><span class="p">,</span> <span class="n">column</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span></code>
 
 </h3>
diff --git a/docs/backend/search/search_index.json b/docs/backend/search/search_index.json
index 67ad520..09fcdca 100644
--- a/docs/backend/search/search_index.json
+++ b/docs/backend/search/search_index.json
@@ -1 +1,78 @@
-{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Home","text":"<p>This is the technical documentation for the backend of the Data Editing plugin. Return to the main documentation.</p>"},{"location":"#EditableEventSourced.EditableEventSourced","title":"<code>EditableEventSourced</code>","text":"<p>This class provides CRUD methods to make a dataset editable using the Event Sourcing pattern: edits are stored in a separate dataset called the editlog. The source dataset is never changed. Both the source dataset and the editlog are used to compute the edited state.</p>"},{"location":"#EditableEventSourced.EditableEventSourced.create_row","title":"<code>create_row(primary_keys, column_values)</code>","text":"<p>Creates a new row.</p> <p>Parameters:</p> Name Type Description Default <code>primary_keys</code> <code>dict</code> <p>A dictionary containing values for all primary keys. The set of values must be unique. Example: {\"id\": \"My new unique id\"}</p> required <code>column_values</code> <code>dict</code> <p>A dictionary containing values for all other columns. Example: {\"col1\": \"hey\", \"col2\": 42, \"col3\": True}</p> required <p>Returns:</p> Name Type Description <code>str</code> <code>str</code> <p>A message indicating that the row was created.</p> Note <p>This method does not implement data validation or check that the values are allowed for the specified columns.</p>"},{"location":"#EditableEventSourced.EditableEventSourced.delete_row","title":"<code>delete_row(primary_keys)</code>","text":"<p>Deletes a row identified by the given primary key(s).</p> <p>Parameters:</p> Name Type Description Default <code>primary_keys</code> <code>dict</code> <p>A dictionary containing the primary key(s) value(s) that identify the row to delete.</p> required <p>Returns:</p> Name Type Description <code>str</code> <p>A message indicating that the row was deleted.</p>"},{"location":"#EditableEventSourced.EditableEventSourced.empty_editlog","title":"<code>empty_editlog()</code>","text":"<p>Writes an empty dataframe to the editlog dataset.</p>"},{"location":"#EditableEventSourced.EditableEventSourced.get_edited_cells_df","title":"<code>get_edited_cells_df()</code>","text":"<p>Returns a pandas DataFrame with the edited cells.</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pandas.DataFrame: A DataFrame containing only the edited rows and columns.</p>"},{"location":"#EditableEventSourced.EditableEventSourced.get_edited_cells_df_indexed","title":"<code>get_edited_cells_df_indexed()</code>","text":"<p>Returns a pandas DataFrame with the edited cells, indexed by the primary keys.</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pandas.DataFrame A DataFrame containing only the edited rows and columns, indexed by the primary keys.</p>"},{"location":"#EditableEventSourced.EditableEventSourced.get_edited_df","title":"<code>get_edited_df()</code>","text":"<p>Returns the original dataframe with any edited values.</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pandas.DataFrame: A DataFrame with the original data and any edited values.</p>"},{"location":"#EditableEventSourced.EditableEventSourced.get_edited_df_indexed","title":"<code>get_edited_df_indexed()</code>","text":"<p>Returns the original dataframe with any edited values, indexed by the primary keys.</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pandas.DataFrame: A DataFrame with the original data and any edited values, indexed by the primary keys.</p>"},{"location":"#EditableEventSourced.EditableEventSourced.get_editlog_df","title":"<code>get_editlog_df()</code>","text":"<p>Returns the contents of the editlog.</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pandas.DataFrame: A DataFrame containing the editlog.</p>"},{"location":"#EditableEventSourced.EditableEventSourced.get_original_df","title":"<code>get_original_df()</code>","text":"<p>Returns the original data without any edits.</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pandas.DataFrame: The original data.</p>"},{"location":"#EditableEventSourced.EditableEventSourced.get_row","title":"<code>get_row(primary_keys)</code>","text":"<p>Retrieve a single row from the dataset that was created, updated or deleted.</p> <p>Parameters:</p> Name Type Description Default <code>primary_keys</code> <code>dict</code> <p>A dictionary containing values for all primary keys defined in the initial data editing setup. The set of values must be unique. Example: {     \"key1\": \"cat\",     \"key2\": \"2022-12-21\", }</p> required <p>Returns:</p> Type Description <p>pandas.DataFrame: A single-row dataframe containing the values of editable columns, indexed by the primary keys. Example:</p> <pre><code>key1        key2        editable_column1    editable_column2\n\"cat\"       2022-12-21  \"hello\"             42\n</code></pre> Notes <ul> <li>The current implementation loads all edited rows in memory, then filters the rows that match the provided primary key values.</li> <li>This method does not read rows that were not edited, and it does not read columns which are not editable.<ul> <li>If some rows of the dataset were created, then by definition all columns are editable (including primary keys).</li> <li>If no row was created, editable columns are those defined in the initial data editing setup.</li> </ul> </li> </ul>"},{"location":"#EditableEventSourced.EditableEventSourced.update_row","title":"<code>update_row(primary_keys, column, value)</code>","text":"<p>Updates a row.</p> <p>Parameters:</p> Name Type Description Default <code>primary_keys</code> <code>dict</code> <p>A dictionary containing primary key(s) value(s) that identify the row to update.</p> required <code>column</code> <code>str</code> <p>The name of the column to update.</p> required <code>value</code> <code>str</code> <p>The value to set for the cell identified by key and column.</p> required <p>Returns:</p> Name Type Description <code>str</code> <code>str</code> <p>A message indicating that the row was edited.</p> Note <p>This method doesn't implement data validation. It doesn't check that the value is allowed for the specified column.</p>"}]}
\ No newline at end of file
+{
+    "config": {
+        "lang": [
+            "en"
+        ],
+        "separator": "[\\s\\-]+",
+        "pipeline": [
+            "stopWordFilter"
+        ]
+    },
+    "docs": [
+        {
+            "location": "",
+            "title": "Home",
+            "text": "<p>This is the technical documentation for the backend of the Visual Edit plugin. Return to the main documentation.</p>"
+        },
+        {
+            "location": "#DataEditor.DataEditor",
+            "title": "<code>DataEditor</code>",
+            "text": "<p>This class provides CRUD methods to make a dataset editable using the Event Sourcing pattern: edits are stored in a separate dataset called the editlog. The source dataset is never changed. Both the source dataset and the editlog are used to compute the edited state.</p>"
+        },
+        {
+            "location": "#DataEditor.DataEditor.create_row",
+            "title": "<code>create_row(primary_keys, column_values)</code>",
+            "text": "<p>Creates a new row.</p> <p>Parameters:</p> Name Type Description Default <code>primary_keys</code> <code>dict</code> <p>A dictionary containing values for all primary keys. The set of values must be unique. Example: {\"id\": \"My new unique id\"}</p> required <code>column_values</code> <code>dict</code> <p>A dictionary containing values for all other columns. Example: {\"col1\": \"hey\", \"col2\": 42, \"col3\": True}</p> required <p>Returns:</p> Name Type Description <code>str</code> <code>str</code> <p>A message indicating that the row was created.</p> Note <p>This method does not implement data validation or check that the values are allowed for the specified columns.</p>"
+        },
+        {
+            "location": "#DataEditor.DataEditor.delete_row",
+            "title": "<code>delete_row(primary_keys)</code>",
+            "text": "<p>Deletes a row identified by the given primary key(s).</p> <p>Parameters:</p> Name Type Description Default <code>primary_keys</code> <code>dict</code> <p>A dictionary containing the primary key(s) value(s) that identify the row to delete.</p> required <p>Returns:</p> Name Type Description <code>str</code> <p>A message indicating that the row was deleted.</p>"
+        },
+        {
+            "location": "#DataEditor.DataEditor.empty_editlog",
+            "title": "<code>empty_editlog()</code>",
+            "text": "<p>Writes an empty dataframe to the editlog dataset.</p>"
+        },
+        {
+            "location": "#DataEditor.DataEditor.get_edited_cells_df",
+            "title": "<code>get_edited_cells_df()</code>",
+            "text": "<p>Returns a pandas DataFrame with the edited cells.</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pandas.DataFrame: A DataFrame containing only the edited rows and columns.</p>"
+        },
+        {
+            "location": "#DataEditor.DataEditor.get_edited_cells_df_indexed",
+            "title": "<code>get_edited_cells_df_indexed()</code>",
+            "text": "<p>Returns a pandas DataFrame with the edited cells, indexed by the primary keys.</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pandas.DataFrame A DataFrame containing only the edited rows and columns, indexed by the primary keys.</p>"
+        },
+        {
+            "location": "#DataEditor.DataEditor.get_edited_df",
+            "title": "<code>get_edited_df()</code>",
+            "text": "<p>Returns the original dataframe with any edited values.</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pandas.DataFrame: A DataFrame with the original data and any edited values.</p>"
+        },
+        {
+            "location": "#DataEditor.DataEditor.get_edited_df_indexed",
+            "title": "<code>get_edited_df_indexed()</code>",
+            "text": "<p>Returns the original dataframe with any edited values, indexed by the primary keys.</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pandas.DataFrame: A DataFrame with the original data and any edited values, indexed by the primary keys.</p>"
+        },
+        {
+            "location": "#DataEditor.DataEditor.get_editlog_df",
+            "title": "<code>get_editlog_df()</code>",
+            "text": "<p>Returns the contents of the editlog.</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pandas.DataFrame: A DataFrame containing the editlog.</p>"
+        },
+        {
+            "location": "#DataEditor.DataEditor.get_original_df",
+            "title": "<code>get_original_df()</code>",
+            "text": "<p>Returns the original data without any edits.</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pandas.DataFrame: The original data.</p>"
+        },
+        {
+            "location": "#DataEditor.DataEditor.get_row",
+            "title": "<code>get_row(primary_keys)</code>",
+            "text": "<p>Retrieve a single row from the dataset that was created, updated or deleted.</p> <p>Parameters:</p> Name Type Description Default <code>primary_keys</code> <code>dict</code> <p>A dictionary containing values for all primary keys defined in the initial Visual Edit setup. The set of values must be unique. Example: {     \"key1\": \"cat\",     \"key2\": \"2022-12-21\", }</p> required <p>Returns:</p> Type Description <p>pandas.DataFrame: A single-row dataframe containing the values of editable columns, indexed by the primary keys. Example:</p> <pre><code>key1        key2        editable_column1    editable_column2\n\"cat\"       2022-12-21  \"hello\"             42\n</code></pre> Notes <ul> <li>The current implementation loads all edited rows in memory, then filters the rows that match the provided primary key values.</li> <li>This method does not read rows that were not edited, and it does not read columns which are not editable.<ul> <li>If some rows of the dataset were created, then by definition all columns are editable (including primary keys).</li> <li>If no row was created, editable columns are those defined in the initial Visual Edit setup.</li> </ul> </li> </ul>"
+        },
+        {
+            "location": "#DataEditor.DataEditor.update_row",
+            "title": "<code>update_row(primary_keys, column, value)</code>",
+            "text": "<p>Updates a row.</p> <p>Parameters:</p> Name Type Description Default <code>primary_keys</code> <code>dict</code> <p>A dictionary containing primary key(s) value(s) that identify the row to update.</p> required <code>column</code> <code>str</code> <p>The name of the column to update.</p> required <code>value</code> <code>str</code> <p>The value to set for the cell identified by key and column.</p> required <p>Returns:</p> Name Type Description <code>str</code> <code>str</code> <p>A message indicating that the row was edited.</p> Note <p>This method doesn't implement data validation. It doesn't check that the value is allowed for the specified column.</p>"
+        }
+    ]
+}
\ No newline at end of file
diff --git a/docs/deploy.md b/docs/deploy.md
index 66c4277..8292ac2 100644
--- a/docs/deploy.md
+++ b/docs/deploy.md
@@ -2,8 +2,8 @@
 
 ## Preliminary remarks
 
-* When deploying a project with a data editing webapp, there would be two different editlog datasets: one on the Design node, one on the Automation node. This way, edits made on the Design node won't have any impact in production. These two editlog datasets would have the same name, but they should be on different data connections.
-* While the datasets in a Dataiku project are typically on analytics database connections and managed by Dataiku, you may prefer to change the connection of the _editlog_ and _editlog\_pivoted_ datasets to an operational database, with constraints for the database used in Automation that would prevent accidental deletions and tampering with edits.
+* When deploying a project with a Visual Edit webapp, there would be two different editlog datasets: one on the Design node, one on the Automation node. This way, edits made on the Design node won't have any impact in production. These two editlog datasets would have the same name, but they should be on different data connections.
+* While the datasets in a Dataiku project are typically on analytics database connections and managed by Dataiku, you may prefer to change the connection of the _editlog_ and _edits_ datasets to an operational database, with constraints for the database used in Automation that would prevent accidental deletions and tampering with edits.
 * We recommend using SQL connections for these datasets.
 
 ## Deployment steps
@@ -113,7 +113,7 @@ When authentication is required:
 * You can restrict access to specified (groups of) users at the project level, via the Project > Security settings: only those with "Read Dashboard" permission will be able to use the webapp (i.e. see and edit data).
 * You can restrict access at the webapp level, via the "Authorized users" property of the Visual Webapp settings. It is presented as a list; if it is not empty, only users whose identifiers are included in the list will be able to use the webapp (i.e. see and edit data).
 
-Remember that the webapp only writes data to the editlog, not to the original dataset (which stays unchanged). The editlog pivoted and the edited datasets can only be changed by running the recipes that build them.
+Remember that the webapp only writes data to the editlog, not to the original dataset (which stays unchanged). The edits and the edited datasets can only be changed by running the recipes that build them.
 
 ## Tips for production usage
 
diff --git a/docs/faq.md b/docs/faq.md
index 2819c61..6156a8e 100644
--- a/docs/faq.md
+++ b/docs/faq.md
@@ -2,7 +2,7 @@
 
 ## Can the webapp be used by several users at the same time?
 
-Several users can view and edit data at the same time, but they won't see each other's edits in real-time; if 2 or more users try to edit the same cell at the same time, all their edits will be logged but only the last edit will make its way to the _editlog\_pivoted_ dataset.
+Several users can view and edit data at the same time, but they won't see each other's edits in real-time; if 2 or more users try to edit the same cell at the same time, all their edits will be logged but only the last edit will make its way to the _edits_ dataset.
 
 ## What happens if I change primary keys or editable columns in the webapp settings?
 
@@ -15,5 +15,5 @@ Several users can view and edit data at the same time, but they won't see each o
     * Otherwise, many rows could be impacted by a single row of the editlog (instead of a single row).
 * Editable column:
   * Add: no impact.
-  * Remove: previous edits on this column won't be taken into account by the webapp / the recipes, and they won't appear in _editlog\_pivoted_ (but they will still be in the editlog).
+  * Remove: previous edits on this column won't be taken into account by the webapp / the recipes, and they won't appear in _edits_ (but they will still be in the editlog).
 
diff --git a/docs/get-started-crud-python-api.md b/docs/get-started-crud-python-api.md
index 3d03b19..722223e 100644
--- a/docs/get-started-crud-python-api.md
+++ b/docs/get-started-crud-python-api.md
@@ -2,7 +2,7 @@
 
 <iframe src="https://www.loom.com/embed/3d899ce5f7544850abe91d088b969331" frameborder="0" webkitallowfullscreen="" mozallowfullscreen="" allowfullscreen="" style="height: 400px; width: 600px"></iframe>
 
-As an alternative to using the Visual Edit Webapp, you can build a custom webapp that uses the CRUD (Create, Read, Update, Delete) methods provided by the `EditableEventSourced` Python class.
+As an alternative to using the Visual Edit Webapp, you can build a custom webapp that uses the CRUD (Create, Read, Update, Delete) methods provided by the `DataEditor` Python class.
 
 Let's take a quick tour of how to use this. We strongly recommend following the [Visual Webapp guide](get-started) first, to get an initial understanding of the plugin's features and concepts.
 
@@ -10,16 +10,16 @@ Let's take a quick tour of how to use this. We strongly recommend following the
 
 When running the code below in a notebook or a webapp in Dataiku, a code environment must be specified. A code environment was created upon installation of the plugin, but it can't be selected from the list of available code environments as it can only be used by the plugin's visual components (Visual Webapp and Recipes). You'll need to create a new code environment and to make sure it contains the same packages as the plugin's.
 
-## Instantiate the `EditableEventSourced` class
+## Instantiate the `DataEditor` class
 
 ```python
 import dataiku
 dataiku.use_plugin_libs("visual-edit")
-EditableEventSourced = dataiku.import_from_plugin("visual-edit", "EditableEventSourced")
+DataEditor = dataiku.import_from_plugin("visual-edit", "DataEditor")
 ```
 
 ```python
-ees = EditableEventSourced.EditableEventSourced(
+de = DataEditor.DataEditor(
     original_ds_name="my_dataset",
     primary_keys=["id"],
     editable_column_names=["existing_editable_col", "new_editable_col"]
@@ -69,7 +69,7 @@ At dataset level:
 
 This example is based on the tshirt orders dataset from the [Basics 101 course](https://academy.dataiku.com/basics-101).
 
-Import `EditableEventSourced` as shown previously, then use it in the Dash app code:
+Import `DataEditor` as shown previously, then use it in the Dash app code:
 
 ```python
 from dash import html, Input, Output
@@ -81,18 +81,20 @@ ORIGINAL_DATASET = "orders"
 PRIMARY_KEYS = ["order_id"]
 EDITABLE_COLUMN_NAMES = ["tshirt_price", "tshirt_quantity"]
 
-ees = EditableEventSourced.EditableEventSourced(
+de = DataEditor.DataEditor(
     original_ds_name=ORIGINAL_DATASET,
     primary_keys=PRIMARY_KEYS,
     editable_column_names=EDITABLE_COLUMN_NAMES,
 )
 
 tabulator_utils = dataiku.import_from_plugin("visual-edit", "tabulator_utils")
-columns = tabulator_utils.get_columns_tabulator(ees)
+tabulator_utils = dataiku.import_from_plugin("visual-edit", "tabulator_utils")
+columns = tabulator_utils.get_columns_tabulator(de)
 
 # Define Dash layout and callbacks
 ###
 
+dash_tabulator = dataiku.import_from_plugin("visual-edit", "dash_tabulator")
 dash_tabulator = dataiku.import_from_plugin("visual-edit", "dash_tabulator")
 
 def serve_layout():
@@ -100,7 +102,7 @@ def serve_layout():
         dash_tabulator.DashTabulator(
             id="quickstart-datatable",
             datasetName=ORIGINAL_DATASET,
-            data=ees.get_edited_df().to_dict("records"),
+            data=de.get_edited_df().to_dict("records"),
             columns=columns
         ),
         html.Div(id="quickstart-output")
@@ -119,7 +121,7 @@ def log_edit(cell):
 
     cell is a dict with the following properties: row, field, value
     """
-    return ees.update_row(cell["row"], cell["field"], cell["value"])
+    return de.update_row(cell["row"], cell["field"], cell["value"])
 ```
 
 ### Example with advanced Tabulator features
@@ -164,6 +166,6 @@ return parseInt(data.tshirt_quantity) + 2;
 
 The implementation of these methods is based on the [Event Sourcing pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/event-sourcing): instead of directly storing the current state of the edited data, we use an append-only store to record the full series of actions on that data. This store is a dataset that we call the "editlog".
 
-When the `EditableEventSourced` class is instantiated on a given dataset, it creates an "editlog" dataset and 2 recipes that create an "editlog pivoted" and an "edited" dataset (if they don't already exist). Those datasets are created on the same connection as the original dataset.
+When the `DataEditor` class is instantiated on a given dataset, it creates an "editlog" dataset and 2 recipes that create an "edits" and an "edited" dataset (if they don't already exist). Those datasets are created on the same connection as the original dataset.
 
-Edits made via CRUD methods instantly add rows to the editlog. The editlog pivoted and the edited datasets are only updated when the recipes that build them are run.
+Edits made via CRUD methods instantly add rows to the editlog. The edits and the edited datasets are only updated when the recipes that build them are run.
diff --git a/docs/get-started.md b/docs/get-started.md
index 5eb89a2..3988315 100644
--- a/docs/get-started.md
+++ b/docs/get-started.md
@@ -51,18 +51,18 @@ Here is an example of what a Visual Edit webapp would look like:
 Their names start with the original dataset's name. Let's review them by their suffix:
 
 1. **_editlog_** is the raw record of all edit events captured by the webapp. It also serves as an audit trail, for governance purposes. The schema of this dataset is fixed, whatever the original dataset. Here is an example: ![](editlog.png)
-2. **_editlog\_pivoted_** is the output of the _pivot-editlog_ recipe (provided by the plugin) and the user-friendly view of edits. In the previous example: ![](editlog_pivoted.png)
+2. **_edits_** is the output of the _replay-edits_ recipe (provided by the plugin) and the user-friendly view of edits. In the previous example: ![](edits.png)
   * Its schema is a subset of the original dataset's: it doesn't have columns that are display-only, but it has the same key columns and the same editable columns, plus a _last\_edit\_date_ column.
   * Its rows are a subset of the original dataset's: it doesn't contain rows where no edits were made.
   * You can think of it as...
     * A "diff" between edited and original data.
     * A dataset of overrides to apply to the original dataset.
     * The result of "replaying" edit events stored in the log: we only see the last edited values.
-3. **_edited_** is the output of the _merge-edits_ recipe (provided by the plugin) that feeds from the original dataset and the _editlog\_pivoted_.
+3. **_edited_** is the output of the _apply-edits_ recipe (provided by the plugin) that feeds from the original dataset and the _edits_.
   * It corresponds to the edited data that you are seeing via the webapp.
   * However, it is not in sync with the webapp: it's up to you to decide when to build it in the Flow.
-  * It contains the same number of rows as in the original dataset. For any given cell identified by its column and primary key values, if a non-empty value is found in _editlog\_pivoted_, this value is used instead of the original one.
-  * Note that, as a result of the above, it is impossible to empty a non-empty cell with the plugin’s Visual Webapp and recipes. This is because empty values in _editlog\_pivoted_ are ignored.
+  * It contains the same number of rows as in the original dataset. For any given cell identified by its column and primary key values, if a non-empty value is found in _edits_, this value is used instead of the original one.
+  * Note that, as a result of the above, it is impossible to empty a non-empty cell with the plugin’s Visual Webapp and recipes. This is because empty values in _edits_ are ignored.
 
 The datasets are created on the same connection as the original dataset. If not already the case, we recommend using a SQL connection for fast and reliable edits. For edits to be recorded by the webapp, this has to be a write connection. If that's not the case, you can change the connection of these datasets as soon as they've been added to the Flow.
 
@@ -84,11 +84,11 @@ Create and run a _reset edits_ scenario with an "Initialize editlog" Step. This
 
 When opening the webapp in your browser, the same code as in the recipes is executed, from the original dataset and the editlog, in order to present an edited view of the data.
 
-Edits made via the webapp instantly add rows to the _editlog_. The _editlog\_pivoted_ and _edited_ datasets are updated only when you run the corresponding recipes.
+Edits made via the webapp instantly add rows to the _editlog_. The _edits_ and _edited_ datasets are updated only when you run the corresponding recipes.
 
 ## Use edits in the Flow
 
-You may want to leverage the _editlog\_pivoted_ dataset to write corrections back to the IT system that holds source data.
+You may want to leverage the _edits_ dataset to write corrections back to the IT system that holds source data.
 
 In most use cases, however, you would first use the _edited_ dataset as input to recipes, for analytics and reporting purposes. Here are a few tips when doing that:
 
diff --git a/docs/install-plugin.md b/docs/install-plugin.md
index 33a0227..c670bb6 100644
--- a/docs/install-plugin.md
+++ b/docs/install-plugin.md
@@ -8,17 +8,20 @@
   * Make sure the "code env" feature is activated; if it's not, use the contents of the requirements file to create your first environment
   * Enable maintenance access so that someone can install the plugin for you
 
-## How to install
-
-* TODO: instructions to install via Git
-* Create the code environment for this plugin
-  * Select the types of containers you plan to use as backend for data editing webapps or for running recipes provided by the plugin. The easiest is to choose All. ![](add_plugin_creating_code_env.png)
-  * ![](add_plugin_creating_code_env_2.png)
-  * ![](add_plugin_creating_code_env_done.png)
-  * ![](add_plugin_creating_code_env_done_2.png)
-  * ![](add_plugin_done.png)
+## How to install the plugin
+
+* From the Plugins page of your Dataiku instance, click on the "Add Plugin" button in the top right corner and choose "Fetch from Git repository":
+  * Repository URL: `git@github.com:dataiku/dss-visual-edit.git`
+  * Development mode: leave unticked
+  * Checkout: `master`
+  * Path in repository: `dss-plugin-visual-edit`
+* Create the code environment for this plugin. Select the types of containers you plan to use as backend for data editing webapps or for running recipes provided by the plugin (the easiest is to choose All).
 * The plugin is installed! ![](plugin_installed.png) We'll see how to use each of its components in the next section.
 
+## How to update the plugin
+
+From the Plugins page of your Dataiku instance, go to the "Installed" tab, find "Visual Edit" in the list and click on the "Update from repository" link. ![](update_plugin_git.png)
+
 ## Next
 
 Check out the guide to [Get started](get-started) for an introduction to the plugin's components and how to use them.
\ No newline at end of file
diff --git a/docs/plugin_installed.png b/docs/plugin_installed.png
index 54bcbd3..721202d 100644
Binary files a/docs/plugin_installed.png and b/docs/plugin_installed.png differ
diff --git a/docs/reviewing.md b/docs/reviewing.md
index 3276901..214418e 100644
--- a/docs/reviewing.md
+++ b/docs/reviewing.md
@@ -21,7 +21,7 @@ The webapp’s backend implements special behavior when a cell from a column nam
 
 This allows the _editlog_ to include not just the information that the row was reviewed, but also the actual values that were reviewed. This is particularly useful when those values were generated by an algorithm, because they may change if the algorithm changes.
 
-As a result, there will be no missing value in the machine-generated and human-reviewed columns that are present in the _editlog\_pivoted_ dataset, for rows marked as Reviewed.
+As a result, there will be no missing value in the machine-generated and human-reviewed columns that are present in the _edits_ dataset, for rows marked as Reviewed.
 
 ## How-to
 
@@ -31,7 +31,7 @@ You must be familiar with the getting started guide and how to use the plugin to
 * **When creating a Visual Edit webapp**: make sure to select all machine-generated columns and feedback columns as editable.
 * **When using the webapp**: you would review values in generated columns (mark as reviewed, or edit values and add notes when necessary) and fill in missing values.
 * **When building the Flow and defining the _update source_ scenario**: you would typically want to notify end-users via email if there is new data to review.
-* **Test with IT**: share the _editlog\_pivoted_ dataset with IT for them to propagate or leverage edits in other IT systems (see the sub use cases mentioned at the beginning of this document); columns of this dataset include primary keys, machine-generated and human-reviewed columns, a boolean Reviewed column, and additional human feedback columns.
+* **Test with IT**: share the _edits_ dataset with IT for them to propagate or leverage edits in other IT systems (see the sub use cases mentioned at the beginning of this document); columns of this dataset include primary keys, machine-generated and human-reviewed columns, a boolean Reviewed column, and additional human feedback columns.
 
 ## Next
 
diff --git a/docs/sample-project-company-resolution.md b/docs/sample-project-company-resolution.md
index c4e9c45..d26265b 100644
--- a/docs/sample-project-company-resolution.md
+++ b/docs/sample-project-company-resolution.md
@@ -80,7 +80,7 @@ id_companies_ref,id_companies_ext,reviewed,comments
 114233,MEIZ,True,""
 ```
 
-* We used the plugin's Merge recipe with `matches` and `corrected_data` as inputs, and created `matches_edited`. This required to set the “primary_keys” custom field of `matches`, so that the Merge recipe knows how to join its inputs.
+* We used the plugin's "Apply Edits" recipe with `matches` and `corrected_data` as inputs, and created `matches_edited`. This required to set the “primary_keys” custom field of `matches`, so that the "Apply Edits" recipe knows how to join its inputs.
 * We added a `to_be_reviewed` column computed by a Prepare recipe, and used it to Split `matches_edited` into two datasets, for certain and uncertain matches.
 * Finally, a 3-way join between the `companies_ref`, `companies_ext` and `matches_certain` produces the output we were looking for: a dataset of companies that has both the `notes` and `fte_count` columns.
 
@@ -95,21 +95,21 @@ Reviewing could be made in a spreadsheet program. It involves:
 
 ### Usage of the plugin's Visual Webapp component
 
-* Set up the webapp (You can find screenshots in the [Getting started guide](https://dataiku.github.io/lca/get-started)):
+* Set up the webapp (You can find screenshots in the [Getting started guide](https://dataiku.github.io/dss-visual-edit/get-started)):
   * Create new Visual Webapp and edit its settings:
     * The dataset to edit is `matches_uncertain`.
     * `id_companies_ext` is an editable column and is set to be a "linked record" (linking to the `id` column of `companies_ext`). It contains values set from the Fuzzy Join but also missing values (when no match was found).
     * There are 2 more editable columns: `approved` and `comments` (that we created as placeholders for reviewing information). Initially, all values are empty.
-* Start webapp. This creates the editlog and editlog_pivoted datasets.
-* Adapt the flow: use `matches_uncertain_editlog_pivoted` as replacement of `corrected_data`.
+* Start webapp. This creates the editlog and edits datasets.
+* Adapt the flow: use `matches_uncertain_edits` as replacement of `corrected_data`.
 * Use webapp.
 * Implement update mechanism:
   * Preliminary remarks:
-    * The webapp uses the same code as the Merge edits recipe that we have in our flow, except that it uses `matches_uncertain` instead of `matches`. Also, it uses a "live" version of the editlog pivoted, instead of the dataset found in the flow and computed from the editlog.
-    * Marking a match as approved doesn't automatically move it from `matches_uncertain` to `matches_certain`. We need to rebuild these datasets. For this, we need to make sure that the editlog_pivoted dataset has been rebuilt.
+    * The webapp uses the same code as the "Apply Edits" recipe that we have in our flow, except that it uses `matches_uncertain` instead of `matches`. Also, it uses a "live" version of the edits dataset, instead of the dataset found in the flow and computed from the editlog.
+    * Marking a match as approved doesn't automatically move it from `matches_uncertain` to `matches_certain`. We need to rebuild these datasets. For this, we need to make sure that the edits dataset has been rebuilt.
   * Steps:
-    * Create an Update scenario with a force-rebuild of `matches_uncertain_editlog_pivoted` and a rebuild of `matches_uncertain`.
-    * Create a dashboard which embeds the webapp and has a button to run that scenario (see screenshot in [Get started > Test the webapp with a business user](https://dataiku.github.io/lca/get-started#test-the-webapp-with-a-business-user)).
+    * Create an Update scenario with a force-rebuild of `matches_uncertain_edits` and a rebuild of `matches_uncertain`.
+    * Create a dashboard which embeds the webapp and has a button to run that scenario (see screenshot in [Get started > Test the webapp with a business user](https://dataiku.github.io/dss-visual-edit/get-started#test-the-webapp-with-a-business-user)).
 
 ## Lifecycle
 
diff --git a/docs/switch-plugins.md b/docs/switch-plugins.md
new file mode 100644
index 0000000..5667a9d
--- /dev/null
+++ b/docs/switch-plugins.md
@@ -0,0 +1,59 @@
+# Switching to the `visual-edit` plugin
+
+**Visual Edit** (plugin id `visual-edit`) is the public release of the previously private plugin **Data Editing** (plugin id `editable-via-webapp`). If you want your projects based on `editable-via-webapp` to switch to using `visual-edit`, follow the steps below:
+
+1. [Install the `visual-edit` plugin](install-plugin) (keep `editable-via-webapp` for now)
+2. Change the `project_key` variable in the code below
+3. Create a bundle of your Dataiku project (to serve as backup if needed)
+4. Run the code below as a script, or block by block in a notebook environment, either from Dataiku DSS or from your local Python environment (provided it has the `dataiku` package installed)
+5. Repeat for all projects where you used `editable-via-webapp`. You can find a list of such projects at `/plugins/editable-via-webapp/usages/` in the Dataiku DSS web interface
+6. Once you've done this for all projects, you can uninstall the `editable-via-webapp` plugin.
+
+```python
+# %%
+# Initialize dataiku client and project
+import dataiku
+
+project_key = "ML_FEEDBACK_LOOP"
+dss_client = dataiku.api_client()
+project = dss_client.get_project(project_key)
+
+# %%
+# Delete plugin recipes
+for recipe in project.list_recipes(as_type="objects"):
+    # print(recipe)
+    recipe_type = recipe.get_settings().type
+    if (
+        recipe_type == "CustomCode_merge-edits"
+        or recipe_type == "CustomCode_pivot-editlog"
+    ):
+        recipe.delete()
+
+# %%
+# Rename all datasets ending with '_editlog_pivoted' to end with '_edits'
+for d in project.list_datasets():
+    dataset_name = d["name"]
+    dataset = project.get_dataset(dataset_name)
+    # if the name ends with '_editlog_pivoted', rename it to change the suffix to '_edits'
+    if dataset_name.endswith("_editlog_pivoted"):
+        new_name = dataset_name.replace("_editlog_pivoted", "_edits")
+        # add to list of datasets to build
+        dataset.rename(new_name)
+        print(f"Renamed {dataset_name} to {new_name}")
+
+# %%
+# Stop visual webapps, change their types, and restart them: this re-creates the recipes we deleted but with the types from the new plugin
+for webapp in project.list_webapps(as_type="objects"):
+    webapp_settings = webapp.get_settings()
+    if (
+        webapp_settings.get_raw()["type"]
+        == "webapp_editable-via-webapp_edit-dataset-records"
+    ):
+        webapp.stop_backend()
+        webapp_settings.get_raw()["type"] = "webapp_visual-edit_visual-edit"
+        webapp_settings.save()
+        print(
+            f"Changed type of {webapp_settings.get_raw()['name']} to 'webapp_visual-edit_visual-edit'"
+        )
+        webapp.start_or_restart_backend()
+```
diff --git a/docs/update_plugin_git.png b/docs/update_plugin_git.png
new file mode 100644
index 0000000..a481585
Binary files /dev/null and b/docs/update_plugin_git.png differ
diff --git a/dss-plugin-visual-edit/custom-fields/edit-schema/custom-fields.json b/dss-plugin-visual-edit/custom-fields/edit-schema/custom-fields.json
index deeb7cb..f0e9789 100644
--- a/dss-plugin-visual-edit/custom-fields/edit-schema/custom-fields.json
+++ b/dss-plugin-visual-edit/custom-fields/edit-schema/custom-fields.json
@@ -1,14 +1,16 @@
 {
     "meta": {
         "label": "Visual Edit",
-        "description": "Properties managed by a Visual Edit webapp. Do not modify directly — use the corresponding webapp's settings to make changes.",
+        "description": "Dataset fields used by the Visual Edit functionality: primary keys, editable columns, linked records (aka foreign keys). Do not modify directly.",
         "icon": "icon-columns"
     },
     "customFields": [
         {
             "applyToObjects": {
                 "mode": "SOME",
-                "includedObjectTypes": ["DATASET", "RECIPE"]
+                "includedObjectTypes": [
+                    "DATASET"
+                ]
             },
             "field": {
                 "name": "webapp_url",
@@ -20,7 +22,9 @@
         {
             "applyToObjects": {
                 "mode": "SOME",
-                "includedObjectTypes": ["DATASET"]
+                "includedObjectTypes": [
+                    "DATASET"
+                ]
             },
             "field": {
                 "name": "original_ds",
@@ -28,10 +32,13 @@
                 "description": "",
                 "type": "DATASET"
             }
-        },{
+        },
+        {
             "applyToObjects": {
                 "mode": "SOME",
-                "includedObjectTypes": ["DATASET"]
+                "includedObjectTypes": [
+                    "DATASET"
+                ]
             },
             "field": {
                 "name": "editlog_ds",
@@ -39,10 +46,13 @@
                 "description": "",
                 "type": "DATASET"
             }
-        },{
+        },
+        {
             "applyToObjects": {
                 "mode": "SOME",
-                "includedObjectTypes": ["DATASET"]
+                "includedObjectTypes": [
+                    "DATASET"
+                ]
             },
             "field": {
                 "name": "primary_keys",
@@ -51,10 +61,13 @@
                 "type": "COLUMNS",
                 "datasetParamName": "original_ds"
             }
-        },{
+        },
+        {
             "applyToObjects": {
                 "mode": "SOME",
-                "includedObjectTypes": ["DATASET"]
+                "includedObjectTypes": [
+                    "DATASET"
+                ]
             },
             "field": {
                 "name": "editable_column_names",
@@ -65,4 +78,4 @@
             }
         }
     ]
-}
+}
\ No newline at end of file
diff --git a/dss-plugin-visual-edit/custom-recipes/merge-edits/recipe.json b/dss-plugin-visual-edit/custom-recipes/apply-edits/recipe.json
similarity index 71%
rename from dss-plugin-visual-edit/custom-recipes/merge-edits/recipe.json
rename to dss-plugin-visual-edit/custom-recipes/apply-edits/recipe.json
index b2c9815..1179f75 100644
--- a/dss-plugin-visual-edit/custom-recipes/merge-edits/recipe.json
+++ b/dss-plugin-visual-edit/custom-recipes/apply-edits/recipe.json
@@ -1,7 +1,7 @@
 {
     "meta": {
-        "label": "Merge edits",
-        "description": "Merge values edited via the webapp into a copy of the original dataset.",
+        "label": "Apply edits",
+        "description": "Copy the original dataset and override cells with values coming from the edits dataset.",
         "icon": "icon-plus"
     },
     "kind": "PYTHON",
@@ -9,21 +9,20 @@
         {
             "name": "original",
             "label": "Original dataset",
-            "description": "Contains the data to be made editable via webapp",
+            "description": "Dataset made available to the Visual Edit functionality",
             "arity": "UNARY",
             "required": true,
             "acceptsDataset": true
         },
         {
-            "name": "editlog_pivoted",
-            "label": "Pivoted edit log",
-            "description": "Created by webapp and associated pivot recipe",
+            "name": "edits",
+            "label": "Edits to apply",
+            "description": "Dataset of edits created by the 'replay-edits' recipe on an editlog",
             "arity": "UNARY",
             "required": true,
             "acceptsDataset": true
         }
     ],
-
     "outputRoles": [
         {
             "name": "edited",
@@ -43,4 +42,4 @@
         }
     ],
     "resourceKeys": []
-}
+}
\ No newline at end of file
diff --git a/dss-plugin-visual-edit/custom-recipes/merge-edits/recipe.py b/dss-plugin-visual-edit/custom-recipes/apply-edits/recipe.py
similarity index 74%
rename from dss-plugin-visual-edit/custom-recipes/merge-edits/recipe.py
rename to dss-plugin-visual-edit/custom-recipes/apply-edits/recipe.py
index 2e9108a..e7bfeae 100644
--- a/dss-plugin-visual-edit/custom-recipes/merge-edits/recipe.py
+++ b/dss-plugin-visual-edit/custom-recipes/apply-edits/recipe.py
@@ -9,7 +9,7 @@
 # import sys
 # sys.path.append('../../python-lib')
 
-from commons import merge_edits_from_log_pivoted_df, get_dataframe
+from commons import apply_edits_from_df, get_dataframe
 
 
 # %% Get recipe parameters
@@ -21,9 +21,9 @@
 original_schema = original_ds.read_schema()
 original_schema_df = DataFrame(original_schema).set_index("name")
 
-pivoted_names = get_input_names_for_role("editlog_pivoted")
-pivoted_datasets = [dataiku.Dataset(name) for name in pivoted_names]
-pivoted_ds = pivoted_datasets[0]
+edits_names = get_input_names_for_role("edits")
+edits_datasets = [dataiku.Dataset(name) for name in edits_names]
+edits_ds = edits_datasets[0]
 
 edited_names = get_output_names_for_role("edited")
 edited_datasets = [dataiku.Dataset(name) for name in edited_names]
@@ -33,13 +33,15 @@
 # %% Read input data
 ###
 
-editlog_pivoted_df = get_dataframe(pivoted_ds) # this dataframe was written by the pivot-editlog recipe which inferred the schema upon writing: let's use this schema when reading this dataset
+edits_df = get_dataframe(
+    edits_ds
+)  # this dataframe was written by the replay-edits recipe which inferred the schema upon writing: let's use this schema when reading this dataset
 
 
 # %% Compute output data
 ###
 
-edited_df = merge_edits_from_log_pivoted_df(original_ds, editlog_pivoted_df)
+edited_df = apply_edits_from_df(original_ds, edits_df)
 
 
 # %% Write output data
diff --git a/dss-plugin-visual-edit/custom-recipes/pivot-editlog/recipe.py b/dss-plugin-visual-edit/custom-recipes/pivot-editlog/recipe.py
deleted file mode 100644
index 1fac68d..0000000
--- a/dss-plugin-visual-edit/custom-recipes/pivot-editlog/recipe.py
+++ /dev/null
@@ -1,38 +0,0 @@
-#%% Imports
-###
-
-import dataiku
-from dataiku.customrecipe import get_input_names_for_role, get_output_names_for_role
-
-# when using interactive execution:
-# import sys
-# sys.path.append('../../python-lib')
-
-from commons import pivot_editlog
-
-
-#%% Get recipe parameters
-###
-
-editlog_names = get_input_names_for_role('editlog')
-editlog_pivoted_names = get_output_names_for_role('editlog_pivoted')
-
-editlog_datasets = [dataiku.Dataset(name) for name in editlog_names]
-editlog_ds = editlog_datasets[0]
-
-editlog_pivoted_datasets = [dataiku.Dataset(name) for name in editlog_pivoted_names]
-editlog_pivoted_ds = editlog_pivoted_datasets[0]
-
-
-#%% Compute output data
-###
-
-primary_keys = editlog_ds.get_config()["customFields"]["primary_keys"]
-editable_column_names = editlog_ds.get_config()["customFields"]["editable_column_names"]
-editlog_pivoted_df = pivot_editlog(editlog_ds, primary_keys, editable_column_names)
-
-
-#%% Write output data
-###
-
-editlog_pivoted_ds.write_dataframe(editlog_pivoted_df, infer_schema=True, dropAndCreate=True) # the schema is inferred upon writing and might be different from that of the original dataset: this will be reconciled by the merge recipe/method
diff --git a/dss-plugin-visual-edit/custom-recipes/pivot-editlog/recipe.json b/dss-plugin-visual-edit/custom-recipes/replay-edits/recipe.json
similarity index 63%
rename from dss-plugin-visual-edit/custom-recipes/pivot-editlog/recipe.json
rename to dss-plugin-visual-edit/custom-recipes/replay-edits/recipe.json
index 032a55e..ff19b64 100644
--- a/dss-plugin-visual-edit/custom-recipes/pivot-editlog/recipe.json
+++ b/dss-plugin-visual-edit/custom-recipes/replay-edits/recipe.json
@@ -1,7 +1,7 @@
 {
     "meta": {
-        "label": "Pivot editlog",
-        "description": "Create a dataset whose rows correspond to unique keys from the editlog, columns are the original dataset's editable columns, and values are the last edits.",
+        "label": "Replay edits",
+        "description": "Create a dataset of edited cells from the raw event log maintained by the Visual Edit functionality. When the same cell was edited multiple times, the last edit is kept.",
         "icon": "icon-repeat"
     },
     "kind": "PYTHON",
@@ -9,7 +9,7 @@
         {
             "name": "editlog",
             "label": "Edit log",
-            "description": "Managed by webapp",
+            "description": "Managed by the Visual Edit functionality",
             "arity": "UNARY",
             "required": true,
             "acceptsDataset": true
@@ -17,9 +17,9 @@
     ],
     "outputRoles": [
         {
-            "name": "editlog_pivoted",
-            "label": "Pivoted edit log",
-            "description": "",
+            "name": "edits",
+            "label": "Edits",
+            "description": "Dataset of edits, containing the edited rows and editable columns",
             "arity": "UNARY",
             "required": true,
             "acceptsDataset": true
@@ -34,4 +34,4 @@
         }
     ],
     "resourceKeys": []
-}
+}
\ No newline at end of file
diff --git a/dss-plugin-visual-edit/custom-recipes/replay-edits/recipe.py b/dss-plugin-visual-edit/custom-recipes/replay-edits/recipe.py
new file mode 100644
index 0000000..3cba2da
--- /dev/null
+++ b/dss-plugin-visual-edit/custom-recipes/replay-edits/recipe.py
@@ -0,0 +1,40 @@
+# %% Imports
+###
+
+import dataiku
+from dataiku.customrecipe import get_input_names_for_role, get_output_names_for_role
+
+# when using interactive execution:
+# import sys
+# sys.path.append('../../python-lib')
+
+from commons import replay_edits
+
+
+# %% Get recipe parameters
+###
+
+editlog_names = get_input_names_for_role("editlog")
+edits_names = get_output_names_for_role("edits")
+
+editlog_datasets = [dataiku.Dataset(name) for name in editlog_names]
+editlog_ds = editlog_datasets[0]
+
+edits_datasets = [dataiku.Dataset(name) for name in edits_names]
+edits_ds = edits_datasets[0]
+
+
+# %% Compute output data
+###
+
+primary_keys = editlog_ds.get_config()["customFields"]["primary_keys"]
+editable_column_names = editlog_ds.get_config()["customFields"]["editable_column_names"]
+edits_df = replay_edits(editlog_ds, primary_keys, editable_column_names)
+
+
+# %% Write output data
+###
+
+edits_ds.write_dataframe(
+    edits_df, infer_schema=True, dropAndCreate=True
+)  # the schema is inferred upon writing and might be different from that of the original dataset: this will be reconciled by the apply-edits recipe/method
diff --git a/dss-plugin-visual-edit/plugin.json b/dss-plugin-visual-edit/plugin.json
index 20ab5d4..1ba4174 100644
--- a/dss-plugin-visual-edit/plugin.json
+++ b/dss-plugin-visual-edit/plugin.json
@@ -3,11 +3,11 @@
     "version": "1.8.12",
     "meta": {
         "label": "Visual Edit",
-        "description": "Edit dataset records via a webapp and replay edits in the Flow",
+        "description": "Build apps to edit records of data within pipelines",
         "author": "Dataiku (Louis Dorard, Julien Hobeika, Fabien Daoulas)",
         "icon": "icon-edit-sign",
         "tags": [],
-        "url": "http://dataiku.github.io/lca/",
+        "url": "http://dataiku.github.io/dss-visual-edit/",
         "licenseInfo": "Apache Software License",
         "supportLevel": "NOT_SUPPORTED"
     }
diff --git a/dss-plugin-visual-edit/python-lib/EditableEventSourced.py b/dss-plugin-visual-edit/python-lib/DataEditor.py
similarity index 88%
rename from dss-plugin-visual-edit/python-lib/EditableEventSourced.py
rename to dss-plugin-visual-edit/python-lib/DataEditor.py
index 434fbf9..bdda516 100644
--- a/dss-plugin-visual-edit/python-lib/EditableEventSourced.py
+++ b/dss-plugin-visual-edit/python-lib/DataEditor.py
@@ -11,8 +11,8 @@
     get_dataframe,
     write_empty_editlog,
     get_display_column_names,
-    merge_edits_from_log_pivoted_df,
-    pivot_editlog,
+    apply_edits_from_df,
+    replay_edits,
     get_key_values_from_dict,
 )
 from webapp.db.editlogs import EditLog, EditLogAppenderFactory
@@ -41,7 +41,7 @@ class EditUnauthorized:
     pass
 
 
-class EditableEventSourced:
+class DataEditor:
     """
     This class provides CRUD methods to make a dataset editable using the Event Sourcing pattern: edits are stored in a separate dataset called the editlog. The source dataset is never changed. Both the source dataset and the editlog are used to compute the edited state.
     """
@@ -105,37 +105,33 @@ def __setup_editlog__(self):
         self.editlog_columns = self.editlog_ds.get_config().get("schema").get("columns")
 
     def __setup_editlog_downstream__(self):
-        editlog_pivoted_ds_creator = DSSManagedDatasetCreationHelper(
-            self.project, self.editlog_pivoted_ds_name
+        edits_ds_creator = DSSManagedDatasetCreationHelper(
+            self.project, self.edits_ds_name
         )
-        if editlog_pivoted_ds_creator.already_exists():
-            logging.debug("Found editlog pivoted")
+        if edits_ds_creator.already_exists():
+            logging.debug("Found edits dataset")
         else:
-            logging.debug("No editlog pivoted found, creating it...")
-            editlog_pivoted_ds_creator.with_store_into(
-                connection=self.__connection_name__
-            )
-            editlog_pivoted_ds_creator.create()
-            self.editlog_pivoted_ds = Dataset(
-                self.editlog_pivoted_ds_name, self.project_key
-            )
+            logging.debug("No edits dataset found, creating it...")
+            edits_ds_creator.with_store_into(connection=self.__connection_name__)
+            edits_ds_creator.create()
+            self.edits_ds = Dataset(self.edits_ds_name, self.project_key)
             logging.debug("Done.")
 
-        pivot_recipe_name = "compute_" + self.editlog_pivoted_ds_name
-        pivot_recipe_creator = DSSRecipeCreator(
-            "CustomCode_pivot-editlog", pivot_recipe_name, self.project
+        replay_recipe_name = "compute_" + self.edits_ds_name
+        replay_recipe_creator = DSSRecipeCreator(
+            "CustomCode_replay-edits", replay_recipe_name, self.project
         )
-        if recipe_already_exists(pivot_recipe_name, self.project):
-            logging.debug("Found recipe to create editlog pivoted")
-            pivot_recipe = self.project.get_recipe(pivot_recipe_name)
+        if recipe_already_exists(replay_recipe_name, self.project):
+            logging.debug("Found recipe to create edits dataset")
+            replay_recipe = self.project.get_recipe(replay_recipe_name)
         else:
-            logging.debug("No recipe to create editlog pivoted, creating it...")
-            pivot_recipe = pivot_recipe_creator.create()
-            pivot_settings = pivot_recipe.get_settings()
-            pivot_settings.add_input("editlog", self.editlog_ds_name)
-            pivot_settings.add_output("editlog_pivoted", self.editlog_pivoted_ds_name)
-            pivot_settings.custom_fields["webapp_url"] = self.webapp_url
-            pivot_settings.save()
+            logging.debug("No recipe to create edits dataset, creating it...")
+            replay_recipe = replay_recipe_creator.create()
+            replay_settings = replay_recipe.get_settings()
+            replay_settings.add_input("editlog", self.editlog_ds_name)
+            replay_settings.add_output("edits", self.edits_ds_name)
+            replay_settings.custom_fields["webapp_url"] = self.webapp_url
+            replay_settings.save()
             logging.debug("Done.")
 
         edited_ds_creator = DSSManagedDatasetCreationHelper(
@@ -151,22 +147,22 @@ def __setup_editlog_downstream__(self):
             self.edited_ds = Dataset(self.edited_ds_name, self.project_key)
             logging.debug("Done.")
 
-        merge_recipe_name = "compute_" + self.edited_ds_name
-        merge_recipe_creator = DSSRecipeCreator(
-            "CustomCode_merge-edits", merge_recipe_name, self.project
+        apply_recipe_name = "compute_" + self.edited_ds_name
+        apply_recipe_creator = DSSRecipeCreator(
+            "CustomCode_apply-edits", apply_recipe_name, self.project
         )
-        if recipe_already_exists(merge_recipe_name, self.project):
+        if recipe_already_exists(apply_recipe_name, self.project):
             logging.debug("Found recipe to create edited dataset")
-            merge_recipe = self.project.get_recipe(merge_recipe_name)
+            apply_recipe = self.project.get_recipe(apply_recipe_name)
         else:
             logging.debug("No recipe to create edited dataset, creating it...")
-            merge_recipe = merge_recipe_creator.create()
-            merge_settings = merge_recipe.get_settings()
-            merge_settings.add_input("original", self.original_ds_name)
-            merge_settings.add_input("editlog_pivoted", self.editlog_pivoted_ds_name)
-            merge_settings.add_output("edited", self.edited_ds_name)
-            merge_settings.custom_fields["webapp_url"] = self.webapp_url
-            merge_settings.save()
+            apply_recipe = apply_recipe_creator.create()
+            apply_settings = apply_recipe.get_settings()
+            apply_settings.add_input("original", self.original_ds_name)
+            apply_settings.add_input("edits", self.edits_ds_name)
+            apply_settings.add_output("edited", self.edited_ds_name)
+            apply_settings.custom_fields["webapp_url"] = self.webapp_url
+            apply_settings.save()
             logging.debug("Done.")
 
     def __init__(
@@ -192,7 +188,7 @@ def __init__(
         self.schema_columns = self.original_ds.get_config().get("schema").get("columns")
 
         self.editlog_ds_name = self.original_ds_name + "_editlog"
-        self.editlog_pivoted_ds_name = self.editlog_ds_name + "_pivoted"
+        self.edits_ds_name = self.original_ds_name + "_edits"
         self.edited_ds_name = self.original_ds_name + "_edited"
 
         self.__connection_name__ = (
@@ -315,9 +311,7 @@ def get_edited_df(self) -> DataFrame:
         Returns:
             pandas.DataFrame: A DataFrame with the original data and any edited values.
         """
-        return merge_edits_from_log_pivoted_df(
-            self.original_ds, self.get_edited_cells_df()
-        )
+        return apply_edits_from_df(self.original_ds, self.get_edited_cells_df())
 
     def get_edited_cells_df_indexed(self) -> DataFrame:
         """
@@ -337,7 +331,7 @@ def get_edited_cells_df(self) -> DataFrame:
             pandas.DataFrame:
                 A DataFrame containing only the edited rows and columns.
         """
-        return pivot_editlog(
+        return replay_edits(
             self.editlog_ds, self.primary_keys, self.editable_column_names
         )
 
@@ -346,7 +340,7 @@ def get_row(self, primary_keys):
         Retrieve a single row from the dataset that was created, updated or deleted.
 
         Args:
-            primary_keys (dict): A dictionary containing values for all primary keys defined in the initial data editing setup. The set of values must be unique. Example:
+            primary_keys (dict): A dictionary containing values for all primary keys defined in the initial Visual Edit setup. The set of values must be unique. Example:
                 {
                     "key1": "cat",
                     "key2": "2022-12-21",
@@ -364,7 +358,7 @@ def get_row(self, primary_keys):
             - The current implementation loads all edited rows in memory, then filters the rows that match the provided primary key values.
             - This method does not read rows that were not edited, and it does not read columns which are not editable.
                 - If some rows of the dataset were created, then by definition all columns are editable (including primary keys).
-                - If no row was created, editable columns are those defined in the initial data editing setup.
+                - If no row was created, editable columns are those defined in the initial Visual Edit setup.
         """
         key = get_key_values_from_dict(primary_keys, self.primary_keys)
         return self.get_edited_cells_df_indexed().loc[key]
diff --git a/dss-plugin-visual-edit/python-lib/commons.py b/dss-plugin-visual-edit/python-lib/commons.py
index 142f949..f66b735 100644
--- a/dss-plugin-visual-edit/python-lib/commons.py
+++ b/dss-plugin-visual-edit/python-lib/commons.py
@@ -6,7 +6,7 @@
 import logging
 
 
-# Editlog utils - used by Empty Editlog step and by EES for initialization of editlog
+# Editlog utils - used by Empty Editlog step and by DataEditor for initialization of editlog
 
 
 def write_empty_editlog(editlog_ds):
@@ -17,10 +17,10 @@ def write_empty_editlog(editlog_ds):
     )
 
 
-# Utils for EES and plugin components (recipes and scenario steps)
+# Utils for DataEditor and plugin components (recipes and scenario steps)
 
 
-# Used by pivot_editlog method below
+# Used by replay_edits method below
 def __unpack_keys__(df, new_key_names, old_key_name="key"):
     if len(new_key_names) == 1:
         df.rename(columns={old_key_name: new_key_names[0]}, inplace=True)
@@ -46,7 +46,7 @@ def __unpack_keys__(df, new_key_names, old_key_name="key"):
             keys_df = concat([keys_df, DataFrame(data=d, index=[i1])])
             i1 += 1
 
-        # 3. Add a column to editlog_pivoted for each key listed in primary_keys
+        # 3. Add a column to edits for each key listed in primary_keys
         df[new_key_names] = keys_df
 
         # 4. Remove the old key column
@@ -81,11 +81,11 @@ def get_dataframe(mydataset):
     return mydataset_df
 
 
-# Used by Pivot recipe and by EES for getting edited cells
+# Used by Replay recipe and by DataEditor for getting edited cells
 
 
-def pivot_editlog(editlog_ds, primary_keys, editable_column_names):
-    # Create empty dataframe with the proper editlog pivoted schema: all primary keys, all editable columns, and "date" column
+def replay_edits(editlog_ds, primary_keys, editable_column_names):
+    # Create empty dataframe with the proper edits dataset schema: all primary keys, all editable columns, and "date" column
     # This helps ensure that the dataframe we return always has the right schema
     # (even if some columns of the input dataset were never edited)
     cols = (
@@ -97,7 +97,7 @@ def pivot_editlog(editlog_ds, primary_keys, editable_column_names):
 
     editlog_df = get_dataframe(editlog_ds)
     if not editlog_df.size:  # i.e. if empty editlog
-        editlog_pivoted_df = all_columns_df
+        edits_df = all_columns_df
     else:
         editlog_df.rename(columns={"date": "edit_date"}, inplace=True)
         editlog_df = __unpack_keys__(editlog_df, primary_keys).sort_values("edit_date")
@@ -123,7 +123,7 @@ def pivot_editlog(editlog_ds, primary_keys, editable_column_names):
             editlog_grouped_first, on=primary_keys
         )
 
-        editlog_pivoted_df = pivot_table(
+        edits_df = pivot_table(
             editlog_df,
             index=primary_keys,
             columns="column_name",
@@ -133,18 +133,18 @@ def pivot_editlog(editlog_ds, primary_keys, editable_column_names):
         ).join(editlog_grouped_df, on=primary_keys)
 
         # Drop any columns from the pivot that may not be one of the editable_column_names
-        for col in editlog_pivoted_df.columns:
+        for col in edits_df.columns:
             if col not in cols:
-                editlog_pivoted_df.drop(columns=[col], inplace=True)
+                edits_df.drop(columns=[col], inplace=True)
 
-        editlog_pivoted_df.reset_index(inplace=True)
+        edits_df.reset_index(inplace=True)
         # this makes sure that all (editable) columns are here and in the right order
-        editlog_pivoted_df = concat([all_columns_df, editlog_pivoted_df])
+        edits_df = concat([all_columns_df, edits_df])
 
-    return editlog_pivoted_df
+    return edits_df
 
 
-# Used by get_original_df below and by EES for init
+# Used by get_original_df below and by DataEditor for init
 def get_display_column_names(schema, primary_keys, editable_column_names):
     return [
         col.get("name")
@@ -153,7 +153,7 @@ def get_display_column_names(schema, primary_keys, editable_column_names):
     ]
 
 
-# Used by EES and by merge_edits_from_log_pivoted_df method below
+# Used by DataEditor and by apply_edits_from_df method below
 def get_original_df(original_ds):
     original_df = get_dataframe(original_ds)
 
@@ -169,7 +169,7 @@ def get_original_df(original_ds):
         schema, primary_keys, editable_column_names
     )
 
-    # make sure that primary keys will be in the same order for original_df and editlog_pivoted_df, and that we'll return a dataframe where editable columns are last
+    # make sure that primary keys will be in the same order for original_df and edits_df, and that we'll return a dataframe where editable columns are last
     return (
         original_df[primary_keys + display_column_names + editable_column_names],
         primary_keys,
@@ -178,63 +178,57 @@ def get_original_df(original_ds):
     )
 
 
-# Used by Merge recipe and by EES for getting edited data
-def merge_edits_from_log_pivoted_df(original_ds, editlog_pivoted_df):
+# Used by Apply recipe and by DataEditor for getting edited data
+def apply_edits_from_df(original_ds, edits_df):
     original_df, primary_keys, display_columns, editable_columns = get_original_df(
         original_ds
     )
-    # this will contain the list of new columns coming from editlog pivoted but not found in the original dataset's schema
+    # this will contain the list of new columns coming from edits dataset but not found in the original dataset's schema
     editable_columns_new = []
 
-    if not editlog_pivoted_df.size:  # i.e. if empty editlog
+    if not edits_df.size:  # i.e. if empty editlog
         edited_df = original_df
     else:
-        created = editlog_pivoted_df["first_action"] == "create"
-        not_deleted = editlog_pivoted_df["last_action"] != "delete"
-        created_df = editlog_pivoted_df[not_deleted & created]
+        created = edits_df["first_action"] == "create"
+        not_deleted = edits_df["last_action"] != "delete"
+        created_df = edits_df[not_deleted & created]
 
-        # Prepare editlog_pivoted_df
+        # Prepare edits_df
         ###
-        editlog_pivoted_df = editlog_pivoted_df[not_deleted & ~created]
+        edits_df = edits_df[not_deleted & ~created]
 
         # Drop columns which are not primary keys nor editable columns
         options.mode.chained_assignment = None  # this helps prevent SettingWithCopyWarnings that are triggered by the drops below
-        if "last_edit_date" in editlog_pivoted_df.columns:
-            editlog_pivoted_df.drop(columns=["last_edit_date"], inplace=True)
-        if "last_action" in editlog_pivoted_df.columns:
-            editlog_pivoted_df.drop(columns=["last_action"], inplace=True)
-        if "first_action" in editlog_pivoted_df.columns:
-            editlog_pivoted_df.drop(columns=["first_action"], inplace=True)
+        if "last_edit_date" in edits_df.columns:
+            edits_df.drop(columns=["last_edit_date"], inplace=True)
+        if "last_action" in edits_df.columns:
+            edits_df.drop(columns=["last_action"], inplace=True)
+        if "first_action" in edits_df.columns:
+            edits_df.drop(columns=["first_action"], inplace=True)
 
         # Change types to match those of original_df
-        for col in editlog_pivoted_df.columns:
+        for col in edits_df.columns:
             original_dtype = original_df[col].dtypes.name
             if col in primary_keys + display_columns + editable_columns:
                 if is_integer_dtype(original_dtype):
                     # there may be missing values so choose a dtype supporting them.
-                    editlog_pivoted_df[col] = editlog_pivoted_df[col].astype(
-                        Int64Dtype()
-                    )
+                    edits_df[col] = edits_df[col].astype(Int64Dtype())
                 elif is_float_dtype(original_dtype):
-                    editlog_pivoted_df[col] = editlog_pivoted_df[col].astype(float)
+                    edits_df[col] = edits_df[col].astype(float)
                 else:
-                    editlog_pivoted_df[col] = editlog_pivoted_df[col].astype(
-                        original_dtype
-                    )
+                    edits_df[col] = edits_df[col].astype(original_dtype)
             else:
                 editable_columns_new.append(col)
 
         original_df.set_index(primary_keys, inplace=True)
-        if (
-            not editlog_pivoted_df.index.name
-        ):  # if index has no name, i.e. it's a range index
-            editlog_pivoted_df.set_index(primary_keys, inplace=True)
+        if not edits_df.index.name:  # if index has no name, i.e. it's a range index
+            edits_df.set_index(primary_keys, inplace=True)
 
-        # "Replay" edits: Join and Merge
+        # Join and "Merge"
         ###
 
         # Join -> this adds _value_last columns
-        edited_df = original_df.join(editlog_pivoted_df, rsuffix="_value_last")
+        edited_df = original_df.join(edits_df, rsuffix="_value_last")
 
         # "Merge" -> this creates _original columns
         # all last_ and first_ columns have already been dropped
diff --git a/dss-plugin-visual-edit/python-lib/docs/index.md b/dss-plugin-visual-edit/python-lib/docs/index.md
index 3c8a3e6..38bc809 100644
--- a/dss-plugin-visual-edit/python-lib/docs/index.md
+++ b/dss-plugin-visual-edit/python-lib/docs/index.md
@@ -1,3 +1,3 @@
-This is the technical documentation for the backend of the Data Editing plugin. [Return to the main documentation](../).
+This is the technical documentation for the backend of the Visual Edit plugin. [Return to the main documentation](../).
 
-::: EditableEventSourced
\ No newline at end of file
+::: DataEditor
\ No newline at end of file
diff --git a/dss-plugin-visual-edit/python-lib/mkdocs.yml b/dss-plugin-visual-edit/python-lib/mkdocs.yml
index a3d68cf..24a1097 100644
--- a/dss-plugin-visual-edit/python-lib/mkdocs.yml
+++ b/dss-plugin-visual-edit/python-lib/mkdocs.yml
@@ -1,4 +1,4 @@
-site_name: Data Editing Backend
+site_name: Visual Edit Backend
 
 theme:
   name: "material"
diff --git a/dss-plugin-visual-edit/python-lib/tabulator_utils.py b/dss-plugin-visual-edit/python-lib/tabulator_utils.py
index 9ae96c2..33447b0 100644
--- a/dss-plugin-visual-edit/python-lib/tabulator_utils.py
+++ b/dss-plugin-visual-edit/python-lib/tabulator_utils.py
@@ -1,6 +1,7 @@
 """
 This file contains functions used to generate the Tabulator columns configuration for a given dataset.
 """
+
 from typing import Union
 from pandas import DataFrame
 from dash_extensions.javascript import Namespace
@@ -11,7 +12,7 @@
 __ns__ = Namespace("myNamespace", "tabulator")
 
 
-def __get_column_tabulator_type__(ees, col_name):
+def __get_column_tabulator_type__(de, col_name):
     # Determine column type as string, boolean, boolean_tick, or number
     # - based on the type given in editschema_manual, if any
     # - the dataset's schema, otherwise
@@ -19,11 +20,11 @@ def __get_column_tabulator_type__(ees, col_name):
 
     t_type = "string"  # default type
     if (
-        not ees.editschema_manual_df.empty
-        and "type" in ees.editschema_manual_df.columns
-        and col_name in ees.editschema_manual_df.index
+        not de.editschema_manual_df.empty
+        and "type" in de.editschema_manual_df.columns
+        and col_name in de.editschema_manual_df.index
     ):
-        editschema_manual_type = ees.editschema_manual_df.loc[col_name, "type"]
+        editschema_manual_type = de.editschema_manual_df.loc[col_name, "type"]
     else:
         editschema_manual_type = None
 
@@ -31,7 +32,7 @@ def __get_column_tabulator_type__(ees, col_name):
     if editschema_manual_type and editschema_manual_type == editschema_manual_type:
         t_type = editschema_manual_type
     else:
-        schema_df = DataFrame(data=ees.schema_columns).set_index("name")
+        schema_df = DataFrame(data=de.schema_columns).set_index("name")
         if "meaning" in schema_df.columns.to_list():
             schema_meaning = schema_df.loc[col_name, "meaning"]
         else:
@@ -198,10 +199,10 @@ def get_values_from_df(
     )
 
 
-def __get_column_tabulator_linked_record__(ees, linked_record_name):
+def __get_column_tabulator_linked_record__(de, linked_record_name):
     """Define Tabulator formatter and editor settings for a column whose type is linked record"""
 
-    linked_records_df = ees.linked_records_df
+    linked_records_df = de.linked_records_df
     linked_ds_name = linked_records_df.loc[linked_record_name, "ds_name"]
     linked_ds_key_column = linked_records_df.loc[linked_record_name, "ds_key"]
     linked_ds_label_column = linked_records_df.loc[linked_record_name, "ds_label"]
@@ -262,20 +263,20 @@ def __get_column_tabulator_linked_record__(ees, linked_record_name):
     return t_col
 
 
-def get_columns_tabulator(ees, freeze_editable_columns=False):
+def get_columns_tabulator(de, freeze_editable_columns=False):
     """Prepare column settings to pass to Tabulator"""
 
     linked_record_names = []
-    if ees.linked_records:
+    if de.linked_records:
         try:
-            linked_records_df = ees.linked_records_df
+            linked_records_df = de.linked_records_df
             linked_record_names = linked_records_df.index.values.tolist()
         except Exception:
             logging.exception("Failed to get linked record names.")
 
     t_cols = []
     for col_name in (
-        ees.primary_keys + ees.display_column_names + ees.editable_column_names
+        de.primary_keys + de.display_column_names + de.editable_column_names
     ):
         # Properties to be shared by all columns
         t_col = {
@@ -287,19 +288,19 @@ def get_columns_tabulator(ees, freeze_editable_columns=False):
         }
 
         # Define formatter and header filters based on type
-        t_type = __get_column_tabulator_type__(ees, col_name)
+        t_type = __get_column_tabulator_type__(de, col_name)
         if col_name not in linked_record_names:
             t_col.update(__get_column_tabulator_formatter__(t_type))
-        if col_name in ees.primary_keys:
+        if col_name in de.primary_keys:
             t_col["frozen"] = True
 
         # Define editor, if it's an editable column
-        if col_name in ees.editable_column_names:
+        if col_name in de.editable_column_names:
             if freeze_editable_columns:
                 t_col["frozen"] = True  # freeze to the right
             if col_name in linked_record_names:
                 t_type = "linked_record"
-                t_col.update(__get_column_tabulator_linked_record__(ees, col_name))
+                t_col.update(__get_column_tabulator_linked_record__(de, col_name))
             else:
                 t_col.update(__get_column_tabulator_editor__(t_type))
         pretty_types = {
diff --git a/dss-plugin-visual-edit/python-lib/tests/EditableEventSourced_tests.py b/dss-plugin-visual-edit/python-lib/tests/DataEditor_tests.py
similarity index 75%
rename from dss-plugin-visual-edit/python-lib/tests/EditableEventSourced_tests.py
rename to dss-plugin-visual-edit/python-lib/tests/DataEditor_tests.py
index 67a710d..b712e4d 100644
--- a/dss-plugin-visual-edit/python-lib/tests/EditableEventSourced_tests.py
+++ b/dss-plugin-visual-edit/python-lib/tests/DataEditor_tests.py
@@ -14,9 +14,9 @@
 original_schema_df = DataFrame(original_schema).set_index("name")
 
 # %%
-from EditableEventSourced import EditableEventSourced
+from DataEditor import DataEditor
 
-ees = EditableEventSourced(
+de = DataEditor(
     original_ds_name=original_ds_name,
     primary_keys=["name"],
     editable_column_names=["address", "machine_type", "household", "dob", "label"],
@@ -28,19 +28,19 @@
 # user = get_user_details() # use this when in the context of a request sent by a client/browser via http
 
 # %%
-ees.get_edited_df()
+de.get_edited_df()
 
 # %%
-ees.create_row(
+de.create_row(
     primary_keys={"name": "New name"}, column_values={"address": "New address"}
 )
 
 # %%
-ees.update_row(primary_keys={"name": "toto"}, column="label", value="hey2")
+de.update_row(primary_keys={"name": "toto"}, column="label", value="hey2")
 # %%
-ees.get_edited_cells_df()
+de.get_edited_cells_df()
 
 
 # %%
-ees.delete_row(primary_keys={"name": "New name"})
+de.delete_row(primary_keys={"name": "New name"})
 # %%
diff --git a/dss-plugin-visual-edit/python-lib/tests/pivot_tests.py b/dss-plugin-visual-edit/python-lib/tests/pivot_tests.py
index a3c7b6f..55a1e9a 100644
--- a/dss-plugin-visual-edit/python-lib/tests/pivot_tests.py
+++ b/dss-plugin-visual-edit/python-lib/tests/pivot_tests.py
@@ -1,13 +1,13 @@
 from __future__ import annotations
 from os import getenv
 from dataiku import Dataset
-from commons import pivot_editlog, merge_edits_from_log_pivoted_df
+from commons import replay_edits, apply_edits_from_df
 
 original_ds_name: str | None = getenv("ORIGINAL_DATASET")
 original_ds = Dataset(original_ds_name)
 editlog_ds = Dataset("" if original_ds_name is None else original_ds_name + "_editlog")
 primary_keys = editlog_ds.get_config()["customFields"]["primary_keys"]
 editable_column_names = editlog_ds.get_config()["customFields"]["editable_column_names"]
-editlog_pivoted_df = pivot_editlog(editlog_ds, primary_keys, editable_column_names)
-edited_df = merge_edits_from_log_pivoted_df(original_ds, editlog_pivoted_df)
+edits_df = replay_edits(editlog_ds, primary_keys, editable_column_names)
+edited_df = apply_edits_from_df(original_ds, edits_df)
 print(edited_df)
diff --git a/dss-plugin-visual-edit/python-lib/editdatasetrecords/__init__.py b/dss-plugin-visual-edit/python-lib/visualedit/__init__.py
similarity index 100%
rename from dss-plugin-visual-edit/python-lib/editdatasetrecords/__init__.py
rename to dss-plugin-visual-edit/python-lib/visualedit/__init__.py
diff --git a/dss-plugin-visual-edit/webapps/visual-edit/backend.py b/dss-plugin-visual-edit/webapps/visual-edit/backend.py
index b54d1e3..0da6ad0 100644
--- a/dss-plugin-visual-edit/webapps/visual-edit/backend.py
+++ b/dss-plugin-visual-edit/webapps/visual-edit/backend.py
@@ -18,11 +18,11 @@
 from commons import get_last_build_date, try_get_user_identifier
 from dash import Dash, Input, Output, State, dcc, html
 from dataiku_utils import get_dataframe_filtered, client as dss_client
-from EditableEventSourced import (
+from DataEditor import (
     EditSuccess,
     EditFailure,
     EditUnauthorized,
-    EditableEventSourced,
+    DataEditor,
 )
 from flask import Flask, jsonify, make_response, request
 from tabulator_utils import get_columns_tabulator, get_values_from_df
@@ -49,7 +49,7 @@
 authorized_users = webapp_config.authorized_users
 original_ds_name = webapp_config.original_ds_name
 
-ees = EditableEventSourced(
+de = DataEditor(
     original_ds_name=original_ds_name,
     project_key=project_key,
     primary_keys=webapp_config.primary_keys,
@@ -60,7 +60,7 @@
 )
 
 
-columns = get_columns_tabulator(ees, webapp_config.freeze_editable_columns)
+columns = get_columns_tabulator(de, webapp_config.freeze_editable_columns)
 
 last_build_date_initial = ""
 last_build_date_ok = False
@@ -125,7 +125,7 @@ def serve_layout():  # This function is called upon loading/refreshing the page
                     id="datatable",
                     datasetName=original_ds_name,
                     columns=columns,
-                    data=ees.get_edited_df().to_dict(
+                    data=de.get_edited_df().to_dict(
                         "records"
                     ),  # this gets the most up-to-date edited data
                     groupBy=webapp_config.group_column_names,
@@ -202,7 +202,7 @@ def add_edit(cell):
     row_dic = cell["row"]
     updated_field = cell["field"]
     updated_value = cell["value"]
-    results = ees.update_row(row_dic, updated_field, updated_value)
+    results = de.update_row(row_dic, updated_field, updated_value)
 
     info = ""
     for res in results:
@@ -221,7 +221,7 @@ def create_endpoint():
     Create a new row.
 
     Params:
-    - primaryKeys: dict containing values for all primary keys defined in the initial data editing setup; the set of values must be unique.
+    - primaryKeys: dict containing values for all primary keys defined in the initial Visual Edit setup; the set of values must be unique.
     - columnValues: dict containing values for all other columns.
 
     Example request JSON:
@@ -244,7 +244,7 @@ def create_endpoint():
     """
     primary_keys_values = request.get_json().get("primaryKeys")
     column_values = request.get_json().get("columnValues")
-    ees.create_row(primary_keys_values, column_values)
+    de.create_row(primary_keys_values, column_values)
     response = jsonify({"msg": "New row created"})
     return response
 
@@ -269,7 +269,7 @@ def read_endpoint():
 
     Returns: JSON representation of the values of editable columns.
     - If some rows of the dataset were created, then by definition all columns are editable (including primary keys) .
-    - If no row was created, editable columns are those defined in the initial data editing setup.
+    - If no row was created, editable columns are those defined in the initial Visual Edit setup.
     - Example API response:
     ```json
     {
@@ -282,7 +282,7 @@ def read_endpoint():
     ```
     """
     primary_keys_values = request.get_json().get("primaryKeys")
-    response = ees.get_row(primary_keys_values).to_json()
+    response = de.get_row(primary_keys_values).to_json()
     return response
 
 
@@ -293,7 +293,7 @@ def read_all_edits_endpoint():
 
     Returns: CSV-formatted dataset with rows that were created or edited, and values of primary key and editable columns. See remarks of the `read` endpoint.
     """
-    response = make_response(ees.get_edited_cells_df_indexed().to_csv())
+    response = make_response(de.get_edited_cells_df_indexed().to_csv())
     response.headers["Content-Disposition"] = (
         "attachment; filename=" + original_ds_name + "_edits.csv"
     )
@@ -332,7 +332,7 @@ def update_endpoint():
         primary_keys_values = request.args.get("primaryKeys", "")
         column = request.args.get("column", "")
         value = request.args.get("value", "")
-    results = ees.update_row(primary_keys_values, column, value)
+    results = de.update_row(primary_keys_values, column, value)
     info = ""
     for res in results:
         info += __edit_result_to_message__(res) + "\n"
@@ -354,7 +354,7 @@ def delete_endpoint():
         primary_keys = request.get_json().get("primaryKeys")
     else:
         primary_keys = request.args.get("primaryKeys", "")
-    result = ees.delete_row(primary_keys)
+    result = de.delete_row(primary_keys)
     response = jsonify({"msg": __edit_result_to_message__(result)})
     return response
 
@@ -381,7 +381,7 @@ def label_endpoint(linked_ds_name):
     label = ""
 
     linked_record: LinkedRecord | None = None
-    for lr in ees.linked_records:
+    for lr in de.linked_records:
         if linked_ds_name == lr.ds_name:
             linked_record = lr
             break
@@ -394,7 +394,7 @@ def label_endpoint(linked_ds_name):
     linked_ds_label = linked_record.ds_label
 
     # Cast provided key value into appropriate type, necessary for integers for example.
-    original_df, primary_keys, display_columns, editable_columns = ees.get_original_df()
+    original_df, primary_keys, display_columns, editable_columns = de.get_original_df()
 
     key_dtype = original_df[linked_record.name].dtype
     try:
@@ -453,7 +453,7 @@ def lookup_endpoint(linked_ds_name):
         n_results = 1000  # show more options if no search term is provided
 
     linked_record: LinkedRecord | None = None
-    for lr in ees.linked_records:
+    for lr in de.linked_records:
         if linked_ds_name == lr.ds_name:
             linked_record = lr
             break