diff --git a/14-tabular/README.md b/14-tabular/README.md
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+Style #14
+==============================
+
+Constraints:
+
+- The input data of the problem is modeled as entities and relations between them
+
+- The data is placed in tables, with each row potentially cross-referencing rows in other tables
+
+- Existence of a relational query engine
+
+- The problem is solved by issuing queries over the tabular data
+
+Possible names:
+
+- Tabular
+- Flatland
+- Relational
diff --git a/14-tabular/tf-14.py b/14-tabular/tf-14.py
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+import sys, re, string, sqlite3
+
+#
+# The relational database of this problem consists of 3 tables:
+# documents, words, characters
+#
+def create_db_schema(connection):
+    c = connection.cursor()
+    c.execute('''CREATE TABLE documents (id INTEGER PRIMARY KEY AUTOINCREMENT, name)''')
+    c.execute('''CREATE TABLE words (id, doc_id, value)''')
+    c.execute('''CREATE TABLE characters (id, word_id, value)''')
+    connection.commit()
+    c.close()
+
+def load_file_into_database(path_to_file, connection):
+    """ Takes the path to a file and loads the contents into the database """
+    def _read_file(path_to_file):
+        """
+        Takes a path to a file and returns the entire contents of the 
+        file as a string
+        """
+        f = open(path_to_file)
+        data = f.read()
+        f.close()
+        return data
+
+    def _filter_chars_and_normalize(str_data):
+        """
+        Takes a string and returns a copy with all nonalphanumeric chars
+        replaced by white space, and all characters lower-cased
+        """
+        pattern = re.compile('[\W_]+')
+        return pattern.sub(' ', str_data).lower()
+
+    def _scan(str_data):
+        """ Takes a string and scans for words, returning a list of words. """
+        return str_data.split()
+
+    def _remove_stop_words(word_list):
+        f = open('../stop_words.txt')
+        stop_words = f.read().split(',')
+        f.close()
+        # add single-letter words
+        stop_words.extend(list(string.ascii_lowercase))
+        return [w for w in word_list if not w in stop_words]
+
+    # The actual work of splitting the input into words
+    words = _remove_stop_words(_scan(_filter_chars_and_normalize(_read_file(path_to_file))))
+
+    # Now let's add data to the database
+    # Add the document itself to the database
+    c = connection.cursor()
+    c.execute("INSERT INTO documents (name) VALUES (?)", (path_to_file,))
+    c.execute("SELECT id from documents WHERE name=?", (path_to_file,))
+    doc_id = c.fetchone()[0]
+
+    # Add the words to the database
+    c.execute("SELECT MAX(id) FROM words")
+    row = c.fetchone()
+    word_id = row[0]
+    if word_id == None:
+        word_id = 0
+    for w in words:
+        c.execute("INSERT INTO words VALUES (?, ?, ?)", (word_id, doc_id, w))
+        # Add the characters to the database
+        char_id = 0
+        for char in w:
+            c.execute("INSERT INTO characters VALUES (?, ?, ?)", (char_id, word_id, char))
+            char_id += 1
+        word_id += 1
+    connection.commit()
+    c.close()
+
+#
+# The main function
+#
+connection = sqlite3.connect(':memory:')
+create_db_schema(connection)
+load_file_into_database(sys.argv[1], connection)
+
+# Now, let's query
+c = connection.cursor()
+c.execute("SELECT value, COUNT(*) as C FROM words GROUP BY value ORDER BY C DESC")
+for i in range(25):
+    row = c.fetchone()
+    if row != None:
+        print row[0] + ' - '  + str(row[1])
+
+connection.close()