Enhance the Normalization chapter

Author: dimitri-yatsenko

book/30-schema-design/055-normalization.ipynb

@@ -285,22 +285,23 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Practical Example: Animal Research Lab\n",
+        "## DataJoint's Workflow Perspective\n",
         "\n",
-        "Let's apply these principles to design a schema for tracking mice in a research lab.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### ❌ Poor Design (Violates Normalization)\n"
+        "A fundamental insight underlying DataJoint's normalization approach: **databases are workflows where downstream data depends on the integrity of upstream data**.\n",
+        "\n",
+        "This workflow-centric view fundamentally shapes normalization principles and explains why DataJoint emphasizes immutability and avoidance of updates.\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
+        "## Practical Example: Animal Research Lab\n",
+        "\n",
+        "Let's apply these principles to design a schema for tracking mice in a research lab.\n",
+        "\n",
+        "### ❌ Poor Design (Violates Normalization)\n",
+        "\n",
         "```python\n",
         "@schema\n",
         "class Mouse(dj.Manual):\n",
@@ -317,17 +318,6 @@
         "```"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## DataJoint's Workflow Perspective\n",
-        "\n",
-        "A fundamental insight underlying DataJoint's normalization approach: **databases are workflows where downstream data depends on the integrity of upstream data**.\n",
-        "\n",
-        "This workflow-centric view fundamentally shapes normalization principles and explains why DataJoint emphasizes immutability and avoidance of updates.\n"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -423,43 +413,6 @@
         "The dependency chain is **explicit** and **enforced**.\n"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### How Workflow Thinking Leads to Normalization Principles\n",
-        "\n",
-        "The workflow perspective directly motivates DataJoint's normalization principles:\n",
-        "\n",
-        "**1. Immutability (INSERT/DELETE, not UPDATE)**\n",
-        "- **Why**: Updates hide broken dependencies in the workflow\n",
-        "- **Workflow view**: Upstream data is \"input\" to downstream computations—changing input invalidates output\n",
-        "- **Solution**: DELETE forces explicit handling of all dependent data\n",
-        "\n",
-        "**2. Separate Changeable Attributes (Rule 3)**  \n",
-        "- **Why**: Time-varying properties represent different states in the workflow\n",
-        "- **Workflow view**: Each state is a distinct input that produces distinct outputs\n",
-        "- **Solution**: Model states as separate records (INSERTs), not updates to existing records\n",
-        "\n",
-        "**3. Entities with Only Intrinsic Properties (Rules 1 & 2)**\n",
-        "- **Why**: Properties of different entities are at different nodes in the dependency graph\n",
-        "- **Workflow view**: Each entity type represents a distinct stage or data type in the pipeline\n",
-        "- **Solution**: Separate entity types into separate tables to make dependencies explicit\n",
-        "\n",
-        "### The Key Insight\n",
-        "\n",
-        "> **In workflow-centric databases, referential integrity isn't just about preventing orphaned records—it's about ensuring computational validity.**\n",
-        "\n",
-        "Foreign keys don't just link data; they represent **data provenance**:\n",
-        "- \"This result was computed FROM this input\"\n",
-        "- \"This analysis is BASED ON this measurement\"  \n",
-        "- \"This conclusion DEPENDS ON this observation\"\n",
-        "\n",
-        "When you UPDATE input data but leave outputs unchanged, you break the provenance chain. The outputs claim to be based on inputs that no longer exist (in their original form).\n",
-        "\n",
-        "**DataJoint's normalization principles ensure that data dependencies remain explicit and enforceable, making workflows scientifically reproducible and computationally sound.**\n"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},