Introduction

Cloud has become a standard in many companies worldwide, even in industries where, just a few years ago, enterprise-grade on-premises solutions were dominant.

The key benefits include streamlined data analytics processes and simplified data management. One of the most significant advantages is the shift from capital expenditures (CapEx) to operational expenditures (OpEx), allowing companies to scale resources dynamically and optimize costs based on actual usage. The cloud reduces administrative overhead related to data management and infrastructure maintenance while providing quick access to cutting-edge technologies.

One of the core tools in the Google Cloud ecosystem is BigQuery—a serverless, fully-managed data warehouse optimized for high-performance analytics on massive datasets.

This time, I explored the Google Skill Boost course "Streaming Analytics into BigQuery".

Course Overview

The course provider describes it as follows:

"(...) Streaming Analytics into BigQuery quest, where you use Pub/Sub, Dataflow and BigQuery together to stream data for analytics."

- cloudskillsboost

The course focuses on building a real-time data pipeline by integrating Google Cloud Pub/Sub, Dataflow (Apache Beam), and BigQuery. Participants will learn how to ingest, transform, and analyze streaming data in a fully managed cloud environment.

Technologies & Topics Covered

• Pub/Sub
• Dataflow (Beam)
• BigQuery
• Cloud Shell

About the Learning Platform

This course is available in the paid version of Google Cloud Skill Boost. It consists of several hands-on labs where participants complete practical tasks, with progress tracked through checkpoints.

All exercises are performed in a real Google Cloud environment—Google automatically provisions a private student account and workspace. Unlike Learning Paths, this course focuses purely on hands-on experience, with text-based instructions and practical exercises.

One aspect I find particularly useful is the time-limited access to the GCP environment. Each lab session has a predefined time frame, typically around an hour per module. This forces users to stay focused and manage their time efficiently.

What to Expect

Streaming data pipelines differ significantly from traditional batch processing. Instead of querying static datasets, data flows continuously through the system, requiring event-driven processing and efficient data ingestion strategies. This course introduces best practices for handling real-time data ingestion using Pub/Sub and Dataflow, ensuring minimal latency and scalability.

Bonus

After completing the course and passing the validation phase, participants earn a Google Cloud Skill Badge for Streaming Analytics. This badge, issued via Credly, verifies their ability to design, deploy, and optimize real-time data processing solutions in Google Cloud.