minor formatting

Author: dimitri-yatsenko

book/20-concepts/00-databases.md

@@ -81,10 +81,12 @@ Modern databases typically separate data management from data use through distin
 ### Common Architectures
 
 **Server-Client Architecture** (most common): A database server program manages all data operations, while client programs (your scripts, applications, notebooks) connect to request data or submit changes. The server enforces all rules and access permissions consistently for every client. This is like a library where the librarian (server) manages the books and enforces checkout policies, while patrons (clients) request materials.
+The two most popular open-source relational database systems: MySQL and Postgres implement a server-client architecture.
 
-**Embedded Databases**: The database engine runs within your application itself—no separate server. This works for single-user applications like mobile apps or desktop software, but doesn't support multiple users accessing shared data simultaneously. SQLite is a common embedded database.
+**Embedded Databases**: The database engine runs within your application itself—no separate server. This works for single-user applications like mobile apps or desktop software, but doesn't support multiple users accessing shared data simultaneously.
+SQLite is a common embedded database @10.14778/3554821.3554842.
 
-**Distributed Databases**: Data and processing are spread across multiple servers working together. This provides high availability and can handle massive scale, but adds significant complexity. Systems like Google Spanner and Amazon DynamoDB use this approach.
+**Distributed Databases**: Data and processing are spread across multiple servers working together. This provides high availability and can handle massive scale, but adds significant complexity. Systems like Google Spanner, Amazon DynamoDB, and CockroachDB use this approach.
 
 For collaborative scientific research, the server-client architecture dominates because it naturally supports multiple researchers working with shared data while maintaining consistent integrity and security rules.
 

book/30-database-design/052-lookup-tables.ipynb

@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,18 +66,8 @@
     "    grade_point = null: decimal(3,2) unsigned     # Corresponding grade point\n",
     "    \"\"\"\n",
     "    contents = [\n",
-    "        ('A', 4.0),\n",
-    "        ('A-', 3.67),\n",
-    "        ('B+', 3.33),\n",
-    "        ('B', 3.00),\n",
-    "        ('B-', 2.67),\n",
-    "        ('C+', 2.33),\n",
-    "        ('C', 2.00),\n",
-    "        ('C-', 1.67),\n",
-    "        ('D+', 1.33),\n",
-    "        ('D', 1.00),\n",
-    "        ('F', 0.00),\n",
-    "        ('I', None)\n",
+    "        ('A', 4.00), ('A-', 3.67), ('B+', 3.33), ('B', 3.00), ('B-', 2.67), ('C+', 2.33),\n",
+    "        ('C', 2.00), ('C-', 1.67), ('D+', 1.33), ('D', 1.00), ('F', 0.00), ('I', None)\n",
     "    ]"
    ]
   },

book/80-examples/070-fractals.ipynb

@@ -34,19 +34,6 @@
     "Each different value of $c$ produces a completely different fractal pattern—some connected, some fragmented into dust-like structures. Small changes to $c$ can produce dramatically different images, making exploration of the parameter space endlessly fascinating."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "87bd1a0f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "from matplotlib import pyplot as plt\n",
-    "import numpy as np\n",
-    "import datajoint as dj"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "64956c79",
@@ -71,6 +58,19 @@
     "The resulting image shows how quickly different regions of the complex plane \"escape\" under iteration, creating the beautiful fractal boundaries we see in Julia sets.\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "87bd1a0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from matplotlib import pyplot as plt\n",
+    "import numpy as np\n",
+    "import datajoint as dj"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 25,
@@ -189,7 +189,7 @@
    "source": [
     "## Image Processing: Denoising Fractals\n",
     "\n",
-    "In real-world applications, fractal generation often involves noisy data. While we're adding noise artificially here for demonstration, similar challenges arise when:\n",
+    "In real-world applications, data acquisition often involves noisy data. While we're adding noise artificially here for demonstration, similar challenges arise when:\n",
     "\n",
     "- Processing images from telescopes or microscopes\n",
     "- Analyzing scientific data with measurement errors\n",
@@ -1010,7 +1010,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "id": "4917c8c2",
    "metadata": {},
    "outputs": [