Improve notebook docs

Author: DarthMax

examples/graph-analytics-serverless-spark.ipynb

@@ -30,10 +30,8 @@
     "\n",
     "The notebook shows how to use the `graphdatascience` Python library to create, manage, and use a GDS Session.\n",
     "\n",
-    "We consider a graph of people and fruits, which we're using as a simple example to show how to connect your AuraDB instance to a GDS Session, run algorithms, and eventually write back your analytical results to the AuraDB database. \n",
-    "We will cover all management operations: creation, listing, and deletion.\n",
-    "\n",
-    "If you are using self managed DB, follow [this example](../graph-analytics-serverless-self-managed)."
+    "We consider a graph of bicycle rentals, which we're using as a simple example to show how project data from Spark to a GDS Session, run algorithms, and eventually retrieving the results back to Spark.\n",
+    "We will cover all management operations: creation, listing, and deletion."
    ]
   },
   {
@@ -157,7 +155,7 @@
     "# Create a GDS session!\n",
     "gds = sessions.get_or_create(\n",
     "    # we give it a representative name\n",
-    "    session_name=\"people_and_fruits\",\n",
+    "    session_name=\"bike_trips\",\n",
     "    memory=SessionMemory.m_2GB,\n",
     "    ttl=timedelta(minutes=30),\n",
     "    cloud_location=CloudLocation(\"gcp\", \"europe-west1\"),\n",
@@ -216,7 +214,7 @@
     "1. Send an action `v2/graph.project.fromTriplets`\n",
     "   This will initialize the import process and allows us to specify the graph name, and settings like `undirected_relationship_types`. It returns a job id, that we need to reference the import job in the following steps.\n",
     "2. Send the data in batches to the arrow server.\n",
-    "3. Send another action called `v2/graph.project.fromTriples.done` to tell the import process that no more data will be send. This will trigger the final graph creation inside the session.\n",
+    "3. Send another action called `v2/graph.project.fromTriplets.done` to tell the import process that no more data will be send. This will trigger the final graph creation inside the session.\n",
     "4. Wait for the import process to reach the `DONE` state.\n",
     "\n",
     "While the overall process is straight forward, we need to somehow tell Spark to"
@@ -295,8 +293,8 @@
    "source": [
     "## Sending the computation result back to Spark\n",
     "\n",
-    "Once the computation is done. We might want to further use the result in Spark.\n",
-    "We can do this in a similar to the projection, by streaming batches of data into each of the Spark workers.\n",
+    "Once the computation is done, we might want to further use the result in Spark.\n",
+    "We can do this in a similar way to the projection, by streaming batches of data into each of the Spark workers.\n",
     "Retrieving the data is a bit more complicated since we need some input data frame in order to trigger computations on the Spark workers.\n",
     "We use a data range equal to the size of workers we have in our cluster as our driving table.\n",
     "On the workers we will disregard the input and instead stream the computation data from the GDS Session."