L6. Big Data and Analytics: BigQuery, Pub/Sub, Dataflow, and Looker
Video generating
Check back soon for the video lesson on Big Data and Analytics: BigQuery, Pub/Sub, Dataflow, and Looker
Google pioneered big data analytics and built enterprise-scale tools available to all Google Cloud customers. The Digital Leader exam tests BigQuery, Pub/Sub, Dataflow, and Looker for data-driven decision making.
BigQuery
BigQuery is Google Cloud's fully managed, serverless, petabyte-scale data warehouse and analytics engine. Key characteristics:
- Serverless: no infrastructure to provision or manage
- Separates storage and compute for independent scaling
- Built-in ML: BigQuery ML lets you create and train ML models using SQL
- Real-time analytics with streaming inserts
- Query pricing: per TB of data scanned (on-demand) or flat-rate slots
- Data sharing: BigQuery datasets can be shared across projects and organizations
Pub/Sub
Cloud Pub/Sub is a fully managed real-time messaging service for decoupling services that produce messages from services that process them. How it works:
- Publishers send messages to a topic
- Subscribers receive messages through subscriptions on that topic
- Messages are durably stored until delivered
Dataflow
Cloud Dataflow is a fully managed service for streaming and batch data processing pipelines, built on Apache Beam. Use for: transforming, cleaning, and enriching data before loading it into BigQuery or other storage. Common pattern: Pub/Sub (ingest) → Dataflow (transform) → BigQuery (analyze).
Looker
Looker is Google Cloud's business intelligence (BI) and data visualization platform. Key capabilities:
- Drag-and-drop data exploration and dashboards
- Embedded analytics in applications
- LookML: Looker's modeling language for defining metrics
Dataproc
Cloud Dataproc is a managed Spark and Hadoop service for running existing Apache workloads. Use when: migrating existing Hadoop/Spark workloads to the cloud.
Analytics Pipeline
A typical Google Cloud analytics architecture:
Data Sources → Pub/Sub (ingest) → Dataflow (transform) → BigQuery (warehouse) → Looker (visualize)
| Service | Purpose |
|---|---|
| BigQuery | Serverless petabyte-scale data warehouse |
| Pub/Sub | Real-time message ingestion and decoupling |
| Dataflow | Stream and batch data processing (Apache Beam) |
| Looker | Business intelligence and dashboards |
| Dataproc | Managed Spark and Hadoop |
- ✓BigQuery is a serverless petabyte-scale data warehouse; storage and compute scale independently
- ✓BigQuery ML lets you train machine learning models using SQL directly in BigQuery
- ✓Pub/Sub is a fully managed messaging service for real-time event ingestion and decoupling
- ✓Dataflow runs Apache Beam pipelines for streaming and batch data transformation
- ✓Common pipeline: Pub/Sub (ingest) → Dataflow (transform) → BigQuery (analyze) → Looker (visualize)
1. A retail company wants to analyze 500 TB of historical sales data using SQL without managing any server infrastructure. Which Google Cloud service is most appropriate?
2. An IoT application generates millions of sensor events per second that must be ingested and processed in real time. Which services form the recommended Google Cloud pipeline?
Recommended: Pluralsight
Reinforce these lessons with Pluralsight's Google Cloud paths: structured video courses, GCP console labs, and practice exams for the Digital Leader certification.