Fix tutorial navigation (timescale#2576)

Author: Loquacity

tutorials/financial-ingest-real-time/financial-ingest-dataset.md

@@ -1,9 +1,11 @@
 ---
 title: Ingest real-time financial websocket data - Set up the dataset
 excerpt: Set up a dataset so you can query financial tick data to analyze price changes
-products: [cloud, mst, self_hosted]
-keywords: [tutorials, finance, learn]
-tags: [tutorials, advanced]
+products: [cloud]
+keywords: [finance, analytics, websockets, data pipeline]
+tags: [tutorials, intermediate]
+layout_components: [next_prev_large]
+content_group: Ingest real-time financial websocket data
 ---
 
 import CreateAndConnect from "versionContent/_partials/_cloud-create-connect-tutorials.mdx";
@@ -26,7 +28,7 @@ regular PostgreSQL table named `company`.
 <Collapsible heading="Connect to the websocket server" defaultExpanded={false}>
 
 When you connect to the Twelve Data API through a websocket, you create a
-persistent connection between your computer and the websocket server. 
+persistent connection between your computer and the websocket server.
 You set up a Python environment, and pass two arguments to create a
 websocket object and establish the connection.
 
@@ -168,11 +170,11 @@ To ingest the data into your Timescale service, you need to implement the
 `on_event` function.
 
 After the websocket connection is set up, you can use the `on_event` function
-to ingest data into the database. This is a data pipeline that ingests real-time 
+to ingest data into the database. This is a data pipeline that ingests real-time
 financial data into your Timescale service.
 
 Stock trades are ingested in real-time Monday through Friday, typically during
-normal trading hours of the New York Stock Exchange (9:30&nbsp;AM to 
+normal trading hours of the New York Stock Exchange (9:30&nbsp;AM to
 4:00&nbsp;PM&nbsp;EST).
 
 <CreateHypertableStocks />
@@ -246,7 +248,7 @@ This function needs to:
             if self.conn is not None:
                 cursor = self.conn.cursor()
                 sql = f"""
-                INSERT INTO {self.DB_TABLE} ({','.join(self.DB_COLUMNS)}) 
+                INSERT INTO {self.DB_TABLE} ({','.join(self.DB_COLUMNS)})
                 VALUES %s;"""
                 execute_values(cursor, sql, data)
                 self.conn.commit()
@@ -274,7 +276,7 @@ This function needs to:
                     print(f"Batch insert #{self.insert_counter}")
                     self.current_batch = []
             def start(self, symbols):
-                """Connect to the web socket server and start streaming real-time data 
+                """Connect to the web socket server and start streaming real-time data
                 into the database.
 
                 Args:
@@ -287,12 +289,12 @@ This function needs to:
                 while True:
                    ws.heartbeat()
                    time.sleep(10)
-        onn = psycopg2.connect(database="tsdb", 
-                            host="<HOST>", 
-                            user="tsdbadmin", 
+        onn = psycopg2.connect(database="tsdb",
+                            host="<HOST>",
+                            user="tsdbadmin",
                             password="<PASSWORD>",
                             port="<PORT>")
-    
+
         symbols = ["BTC/USD", "ETH/USD", "MSFT", "AAPL"]
         websocket = WebsocketPipeline(conn)
         websocket.start(symbols=symbols)
@@ -333,4 +335,4 @@ the energy consumption dataset.
 </Collapsible>
 
 [twelve-wrapper]: https://github.com/twelvedata/twelvedata-python
-[psycopg2]: https://www.psycopg.org/docs/
+[psycopg2]: https://www.psycopg.org/docs/

tutorials/financial-ingest-real-time/financial-ingest-query.md

@@ -1,9 +1,11 @@
 ---
 title: Ingest real-time financial websocket data - Query the data
 excerpt: Create candlestick views and query financial tick data to analyze price changes
-products: [cloud, mst, self_hosted]
-keywords: [tutorials, finance, learn]
-tags: [tutorials, advanced]
+products: [cloud]
+keywords: [finance, analytics, websockets, data pipeline]
+tags: [tutorials, intermediate]
+layout_components: [next_prev_large]
+content_group: Ingest real-time financial websocket data
 ---
 
 import GraphOhlcv from "versionContent/_partials/_graphing-ohlcv-data.mdx";
@@ -80,14 +82,14 @@ OHLCV values.
     ```sql
              bucket         | symbol  |  open   |  high   |   low   |  close  | day_volume
     ------------------------+---------+---------+---------+---------+---------+------------
-     2023-05-30 08:00:00+00 | AAPL   | 176.31 | 176.31 |    176 | 176.01 |           
-     2023-05-30 08:01:00+00 | AAPL   | 176.27 | 176.27 | 176.02 |  176.2 |           
-     2023-05-30 08:06:00+00 | AAPL   | 176.03 | 176.04 | 175.95 |    176 |           
-     2023-05-30 08:07:00+00 | AAPL   | 175.95 |    176 | 175.82 | 175.91 |           
-     2023-05-30 08:08:00+00 | AAPL   | 175.92 | 176.02 |  175.8 | 176.02 |           
-     2023-05-30 08:09:00+00 | AAPL   | 176.02 | 176.02 |  175.9 | 175.98 |           
-     2023-05-30 08:10:00+00 | AAPL   | 175.98 | 175.98 | 175.94 | 175.94 |           
-     2023-05-30 08:11:00+00 | AAPL   | 175.94 | 175.94 | 175.91 | 175.91 |           
+     2023-05-30 08:00:00+00 | AAPL   | 176.31 | 176.31 |    176 | 176.01 |
+     2023-05-30 08:01:00+00 | AAPL   | 176.27 | 176.27 | 176.02 |  176.2 |
+     2023-05-30 08:06:00+00 | AAPL   | 176.03 | 176.04 | 175.95 |    176 |
+     2023-05-30 08:07:00+00 | AAPL   | 175.95 |    176 | 175.82 | 175.91 |
+     2023-05-30 08:08:00+00 | AAPL   | 175.92 | 176.02 |  175.8 | 176.02 |
+     2023-05-30 08:09:00+00 | AAPL   | 176.02 | 176.02 |  175.9 | 175.98 |
+     2023-05-30 08:10:00+00 | AAPL   | 175.98 | 175.98 | 175.94 | 175.94 |
+     2023-05-30 08:11:00+00 | AAPL   | 175.94 | 175.94 | 175.91 | 175.91 |
      2023-05-30 08:12:00+00 | AAPL   |  175.9 | 175.94 |  175.9 | 175.94 |
     ```
 

tutorials/financial-ingest-real-time/index.md

@@ -1,8 +1,11 @@
 ---
 title: Ingest real-time financial websocket data
 excerpt: Set up a data pipeline to get data from different financial APIs
-products: [cloud, mst, self_hosted]
+products: [cloud]
 keywords: [finance, analytics, websockets, data pipeline]
+tags: [tutorials, intermediate]
+layout_components: [next_prev_large]
+content_group: Ingest real-time financial websocket data
 ---
 
 import CandlestickIntro from "versionContent/_partials/_candlestick_intro.mdx";

tutorials/nyc-taxi-geospatial/dataset-nyc.md

@@ -1,11 +1,11 @@
 ---
-title: Query time-series data tutorial - set up dataset
-excerpt: Set up a dataset so you can query time-series data
-products: [cloud, mst, self_hosted]
-keywords: [tutorials, create, dataset]
-tags: [tutorials, beginner]
+title: Plot geospatial time-series data tutorial - set up dataset
+excerpt: Set up a dataset so you can query geospatial time-series data
+products: [cloud]
+keywords: [tutorials, GIS, geospatial, learn]
+tags: [tutorials, intermediate]
 layout_components: [next_prev_large]
-content_group: Analyze NYC taxi cab data
+content_group: Plot geospatial NYC taxi cab data
 ---
 
 import CreateAndConnect from "versionContent/_partials/_cloud-create-connect-tutorials.mdx";

tutorials/nyc-taxi-geospatial/plot-nyc.md

@@ -1,11 +1,11 @@
 ---
-title: Query time-series data tutorial - query the data
-excerpt: Query time-series data
-products: [cloud, mst, self_hosted]
-keywords: [tutorials, query]
-tags: [tutorials, beginner]
+title: Plot geospatial time-series data tutorial - query the data
+excerpt: Query geospatial time-series data
+products: [cloud]
+keywords: [tutorials, GIS, geospatial, learn]
+tags: [tutorials, intermediate]
 layout_components: [next_prev_large]
-content_group: Analyze NYC taxi cab data
+content_group: Plot geospatial NYC taxi cab data
 ---
 
 # Query the data