Google Cloud Platform IoT Analytics

Google Cloud Platform IoT refers to a set of tools you can use to collect, process, and analyze data from connected devices. Although Cloud IoT Core (the main service for device connectivity) was shut down in 2023, the core analytics tools on Google Cloud are still widely used for building custom IoT solutions. 

Instead of relying on a single IoT platform, developers now combine various GCP services to build pipelines for data ingestion, processing, and analysis. This modular approach is flexible and allows you to integrate third-party device gateways or run your own edge logic. 

 Main Tools for Google Cloud Platform IoT Analytics 

1. Custom Gateways for Device Data 

Since Cloud IoT Core is no longer available, IoT devices now connect to Google Cloud through custom-built gateways. These are applications or services that receive data from devices over protocols like MQTT or HTTP, then forward that data to Google Cloud using REST APIs or Cloud Pub/Sub. 

These gateways often run on edge devices or VMs and handle encryption, authentication, and data formatting. 

2. Cloud Pub/Sub – Messaging Between Services 

Cloud Pub/Sub is used to send and receive messages between different parts of your system. It's often the first step in the cloud pipeline for IoT data. 

• Publishers (like your gateway) send messages to a topic. 

• Subscribers (like a processing service) receive those messages. 

You can use Pub/Sub to decouple services and handle data from thousands of devices at once. It supports high throughput and can buffer spikes in traffic. 

3. Cloud Dataflow – Data Processing 

Cloud Dataflow processes the incoming data. It can handle both real-time streams and batch jobs. 

For IoT use cases, it’s commonly used to: 

• Parse and clean sensor data 

• Convert units or add timestamps 

• Detect patterns or drop noisy signals 

• Aggregate data over time windows 

Dataflow uses the Apache Beam model, so you write one pipeline and can run it in either streaming or batch mode depending on your needs. 

4. BigQuery – Analytics and Storage 

BigQuery is a cloud-based data warehouse where you can store processed IoT data and run queries using SQL. 

Once data is cleaned and transformed, it’s written to BigQuery tables. From there, you can: 

• Run queries to analyze trends or behaviors 

• Join device data with business data 

• Create dashboards or reports 

• Schedule recurring queries or alerts 

BigQuery also supports real-time inserts, so your data is available for analysis almost immediately after processing. 

Example Workflow 

Here’s a simple example of how these tools work together in a cloud IoT setup: 

1. Devices send data (temperature, GPS, etc.) to a local gateway. 

2. The gateway sends the data to Cloud Pub/Sub. 

3. Cloud Dataflow subscribes to the Pub/Sub topic, processes the data, and writes it to BigQuery. 

4. You query the data in BigQuery or visualize it using a tool like Looker Studio. 

This setup can scale from a handful of devices to tens of thousands. 

Learn more: https://cloud.google.com/