Data Transformation with Google Cloud
Data is the fuel of digital transformation, and this is the most heavily weighted topic on the exam. This chapter covers the data lifecycle, the difference between structured and unstructured data, and the core Google Cloud data services for storing, streaming, and analyzing information. Knowing which tool fits which job is the key skill.
The data lifecycle and data types
Data typically moves through a lifecycle: it is ingested from sources, stored, processed or transformed, and finally analyzed to produce insights. Matching the right Google Cloud service to each stage is a common exam theme. Data also comes in different forms. Structured data fits neatly into rows and columns with a defined schema, such as a table of orders. Unstructured data has no predefined schema, such as images, audio recordings, videos, and free-form text. Semi-structured data, like JSON, sits in between. These types are stored and processed with different tools, so recognizing them guides your service choices.
Databases: Cloud SQL and Cloud Spanner
Cloud SQL is a fully managed relational database supporting MySQL, PostgreSQL, and SQL Server. It is ideal for transactional (OLTP) workloads and for migrating existing relational applications with minimal changes, because Google handles patching, backups, and replication. Cloud Spanner is a fully managed relational database that adds horizontal scalability and strong consistency across regions, even globally. Choose Spanner when an application needs both the structure of SQL and the ability to scale massively worldwide without giving up consistency. Cloud SQL suits most standard relational needs, while Spanner targets global scale.
Analytics with BigQuery and Looker
BigQuery is a fully managed, serverless data warehouse built for fast SQL analytics over huge datasets, scaling to petabytes without managing servers or clusters. It separates storage from compute and is the go-to service for analytical (OLAP) queries on historical data. Looker is Google Cloud's business intelligence and data-visualization platform. It lets analysts model consistent business metrics, build governed self-service dashboards, and explore data that lives in warehouses like BigQuery. Together, BigQuery provides the analytical engine and Looker provides the trusted layer where business users explore and visualize results.
Streaming data with Pub/Sub
Pub/Sub is a scalable, asynchronous messaging service that decouples the systems that produce data (publishers) from the systems that consume it (subscribers). It is designed to ingest real-time event streams, such as messages from thousands of IoT sensors or clickstream events, and reliably fan them out to multiple downstream systems. By buffering and distributing messages, Pub/Sub lets pipelines absorb spikes and keeps producers and consumers independent. A common pattern combines Pub/Sub for ingestion, a processing service for transformation, and BigQuery for analysis, illustrating how the data services fit together across the lifecycle.