feat(dwh): update

Author: SamyOubouaziz

pages/data-warehouse/index.mdx

@@ -37,13 +37,6 @@ meta:
         url="/data-warehouse/how-to/"
     />
     <SummaryCard
-        title="How-tos"
-        icon="help-circle-outline"
-        description="Check our guides to creating, using, and managing Data Warehouse for ClickHouse® deployments and their features."
-        label="View How-tos"
-        url="/data-warehouse/how-to/"
-    />
-        <SummaryCard
         title="Additional Content"
         icon="book-open-outline"
         description="Go further with detailed, in-depth information on Data Warehouse for ClickHouse®."

pages/data-warehouse/quickstart.mdx

@@ -85,93 +85,108 @@ You are now connected to your Data Warehouse for ClickHouse® deployment and can
 
 ## How to import and query an example data set
 
-### Creating a database and ingesting data
-
 <Message type="note">
-This example is based on the **New York Taxi Data** from the [Official ClickHouse documentation](https://clickhouse.com/docs/getting-started/example-datasets/nyc-taxi).
+This example is based on the **Environmental Sensors Data** from the [Official ClickHouse® documentation](https://clickhouse.com/docs/getting-started/example-datasets/environmental-sensors).
 </Message>
 
-1. Run the command below to create a new database:
-    ```sql
-    CREATE DATABASE nyc_taxi;
-    ```
+### Creating a database and ingesting data
 
-2. Create a new table in the database you just created:
+1. Run the command below to create a new table in the default database:
     ```sql
-    CREATE TABLE nyc_taxi.trips_small (
-        trip_id             UInt32,
-        pickup_datetime     DateTime,
-        dropoff_datetime    DateTime,
-        pickup_longitude    Nullable(Float64),
-        pickup_latitude     Nullable(Float64),
-        dropoff_longitude   Nullable(Float64),
-        dropoff_latitude    Nullable(Float64),
-        passenger_count     UInt8,
-        trip_distance       Float32,
-        fare_amount         Float32,
-        extra               Float32,
-        tip_amount          Float32,
-        tolls_amount        Float32,
-        total_amount        Float32,
-        payment_type        Enum('CSH' = 1, 'CRE' = 2, 'NOC' = 3, 'DIS' = 4, 'UNK' = 5),
-        pickup_ntaname      LowCardinality(String),
-        dropoff_ntaname     LowCardinality(String)
+    CREATE TABLE sensors
+    (
+        sensor_id UInt16,
+        sensor_type Enum('BME280', 'BMP180', 'BMP280', 'DHT22', 'DS18B20', 'HPM', 'HTU21D', 'PMS1003', 'PMS3003', 'PMS5003', 'PMS6003', 'PMS7003', 'PPD42NS', 'SDS011'),
+        location UInt32,
+        lat Float32,
+        lon Float32,
+        timestamp DateTime,
+        P1 Float32,
+        P2 Float32,
+        P0 Float32,
+        durP1 Float32,
+        ratioP1 Float32,
+        durP2 Float32,
+        ratioP2 Float32,
+        pressure Float32,
+        altitude Float32,
+        pressure_sealevel Float32,
+        temperature Float32,
+        humidity Float32,
+        date Date MATERIALIZED toDate(timestamp)
     )
     ENGINE = MergeTree
-    PRIMARY KEY (pickup_datetime, dropoff_datetime);
+    ORDER BY (timestamp, sensor_id);
+
     ```
 
-3. Insert data from an Amazon S3 bucket 
+2. Insert data in the table you just created:
     ```sql
-    INSERT INTO nyc_taxi.trips_small
-    SELECT
-        trip_id,
-        pickup_datetime,
-        dropoff_datetime,
-        pickup_longitude,
-        pickup_latitude,
-        dropoff_longitude,
-        dropoff_latitude,
-        passenger_count,
-        trip_distance,
-        fare_amount,
-        extra,
-        tip_amount,
-        tolls_amount,
-        total_amount,
-        payment_type,
-        pickup_ntaname,
-        dropoff_ntaname
-    FROM s3(
-        'https://datasets-documentation.s3.eu-west-3.amazonaws.com/nyc-taxi/trips_{0..2}.gz',
-        'TabSeparatedWithNames'
-    );
+    INSERT INTO sensors
+        SELECT *
+        FROM s3Cluster(
+            'default',
+            'https://datawarehouse-samples.s3.fr-par.scw.cloud/2019-06_bmp180.csv.zst',
+            'CSVWithNames',
+            $$ sensor_id UInt16,
+            sensor_type String,
+            location UInt32,
+            lat Float32,
+            lon Float32,
+            timestamp DateTime,
+            P1 Float32,
+            P2 Float32,
+            P0 Float32,
+            durP1 Float32,
+            ratioP1 Float32,
+            durP2 Float32,
+            ratioP2 Float32,
+            pressure Float32,
+            altitude Float32,
+            pressure_sealevel Float32,
+            temperature Float32,
+            humidity Float32 $$
+        )
+    SETTINGS
+        format_csv_delimiter = ';',
+        input_format_allow_errors_ratio = '0.5',
+        input_format_allow_errors_num = 10000,
+        input_format_parallel_parsing = 0,
+        date_time_input_format = 'best_effort',
+        max_insert_threads = 32,
+        parallel_distributed_insert_select = 1;
+
     ```
 
 ### Querying the database
 
 1. Run the command below to count the rows you inserted in the table:
     ```sql
     SELECT count()
-    FROM nyc_taxi.trips_small;
+    FROM sensors;
     ```
 
 2. Run the command below to list the 10 first rows of your table:
     ```sql
     SELECT *
-    FROM nyc_taxi.trips_small
+    FROM sensors
     LIMIT 10;
     ```
 
-3. Run the command below to display the 10 first neighborhoods with the most frequent pickups:
+3. Run the command below to display the storage used by the `sensors` table:
     ```sql
     SELECT
-      pickup_ntaname,
-      count(*) AS count
-    FROM nyc_taxi.trips_small WHERE pickup_ntaname != ''
-    GROUP BY pickup_ntaname
-    ORDER BY count DESC
-    LIMIT 10;
+        disk_name,
+        formatReadableSize(sum(data_compressed_bytes) AS size) AS compressed,
+        formatReadableSize(sum(data_uncompressed_bytes) AS usize) AS uncompressed,
+        round(usize / size, 2) AS compr_rate,
+        sum(rows) AS rows,
+        count() AS part_count
+    FROM system.parts
+    WHERE (active = 1) AND (table = 'sensors')
+    GROUP BY
+        disk_name
+    ORDER BY size DESC;
     ```
 
 To perform more in-depth tests with larger data sets, refer to our [dedicated documentation](/data-warehouse/reference-content/example-datasets/).