Unit 2 roadmap — Exploring Data Transformation with Google Cloud

Every objective below is quoted from Google's Cloud Digital Leader exam guide, retrieved 2026-09-22. Under each topic is that topic's own summary of what the exam actually tests, taken from its lecture.

Topic 1 — The Value of Data

CDL-U2.T1 · 6 objectives · lecture deck of 13 slides

1. Explain how data generates business insights, drives decision making, and creates new value.
2. Differentiate between basic data management concepts, in particular: databases; data warehouses; data lakes.
3. Explain how organizations can create value by using their current data, collecting new data, and sourcing data externally.
4. Describe how the cloud unlocks business value from all types of data, including structured data and previously untapped unstructured data.
5. Discuss the main data value chain concepts and terms.
6. Explain how data governance is essential to a successful data journey.

What this topic actually tests. Which store? database for related records, warehouse for known reporting, lake for raw and unstructured data. Which source? integrate what you hold, stream what you lack, subscribe to what others hold. Which stage? value is realized at action, not ingestion. What does governance do? controls that make greater access safe.

Topic 2 — Google Cloud Data Management Solutions

CDL-U2.T2 · 5 objectives · lecture deck of 12 slides

1. Differentiate between Google Cloud data management options including data type and common business use case, including: Cloud Storage; Cloud Spanner; Cloud SQL; Cloud Bigtable; BigQuery; Firestore.
2. Define key data management concepts and terms, including: relational; non-relational; object storage; structured query language (SQL); NoSQL.
3. Describe the benefits of using BigQuery as a serverless, managed data warehouse and analytics engine that can be used in a multicloud environment.
4. Differentiate between storage classes in Cloud Storage regarding cost and frequency of access, including: Standard; Nearline; Coldline; Archive.
5. Describe the ways that an organization can migrate or modernize their current database in the cloud.

What this topic actually tests. Which shape is the data? objects, relational, documents, keyed series, or analysis. Relational at what scale? Cloud SQL, or Spanner for global consistency. Serve or analyze? Bigtable serves; BigQuery analyzes. How often is it read? monthly, quarterly, yearly — and colder costs more to read, never offline. What changes in the move? the engine, and separately the downtime.

Topic 3 — Making Data Useful and Accessible

CDL-U2.T3 · 4 objectives · lecture deck of 12 slides

1. Describe how Looker democratizes access to data by empowering individuals to self-serve business intelligence and create insights.
2. Discuss the value of analyzing and visualizing data from BigQuery in Looker to create real-time reports, dashboards, and integrating data into workflows.
3. Describe how streaming analytics in real time makes data more useful and generates business value.
4. Describe the main Google Cloud products that modernize data pipelines, including Pub/Sub and Dataflow.

What this topic actually tests. Who writes the SQL? the LookML model and the SQL generator, not the business user. Is a dashboard enough? alerts and deliveries put data into workflows. Batch or stream? ask how fast the data goes stale. Which product? Pub/Sub ingests, Dataflow transforms, BigQuery stores, Looker presents.