Create a Streaming Data Pipeline on GCP with Cloud Pub/Sub, Dataflow, and BigQuery

Enable Pub/Sub and Dataflow APIs:

gcloud services enable dataflow.googleapis.com
gcloud services enable pubsub.googleapis.com

Create a Cloud Storage bucket for Dataflow staging:

gsutil mb gs://$DEVSHELL_PROJECT_ID

Download the GitHub repository used for lab resources:

cd ~
git clone https://github.com/ACloudGuru-Resources/googledataengineer

gcloud pubsub topics create sandiego

Create a BigQuery dataset to stream data into:

bq mk --dataset $DEVSHELL_PROJECT_ID:demos

The table will be named average_speeds. We do not create the table, but Dataflow will create it within the dataset for us.

We will not be interacting with the template directly. We will be using a script that will install the Java environment and execute the template as a Dataflow job:

vim googledataengineer/courses/streaming/process/sandiego/src/main/java/com/google/cloud/training/dataanalyst/sandiego/AverageSpeeds.java

Go to the Dataflow job script directory:

cd ~/googledataengineer/courses/streaming/process/sandiego 

Execute the script that creates the Dataflow streaming job, and subscribe to the Pub/Sub topic.

This script passes along the Project ID, staging bucket (also the Project ID), and the name of the Java template to use:

./run_oncloud.sh $DEVSHELL_PROJECT_ID $DEVSHELL_PROJECT_ID AverageSpeeds

When complete, the streaming job will be subscribed to our Pub/Sub topic, and waiting for streaming input from our simulated sensor data.

Browse to the Python script directory:

cd ~/googledataengineer/courses/streaming/publish

Install any requirements for the Python script:

pip install -U google-cloud-pubsub

Download the simulated sensor data:

gsutil cp gs://la-gcloud-course-resources/sandiego/sensor_obs2008.csv.gz .

Execute the Python script to publish simulated streaming data to Pub/Sub:

./send_sensor_data.py --speedFactor=60 --project=$DEVSHELL_PROJECT_ID

In BigQuery, execute the following query to view the current streamed data, both in the table and in the streaming buffer:

SELECT *
FROM `demos.average_speeds` LIMIT 1000

Notice the total count of records at the bottom. Wait about a minute and run the same query again (be sure to uncheck use cached results in query options) and notice that the number has increased.

Let's get some use out of this data. If we wanted to forecast some necessary road maintenance, we would need to know which lanes have the most traffic, to know which ones will require resurfacing first.

Enter the following query to view which lanes have the most sensor counts:

SELECT lane, count(lane) as total
FROM `demos.average_speeds`
GROUP BY lane
ORDER BY total DESC

We can also view which lanes have the highest average speeds:

SELECT lane, avg(speed) as average_speed
FROM `demos.average_speeds`
GROUP BY lane
ORDER BY average_speed DESC