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This immersive course teaches how to ingest, prepare, transform, analyze, secure, and operationalize data on Google Cloud. You’ll learn to choose appropriate storage services, design ETL/ELT pipelines (batch and streaming), write performant BigQuery SQL, create dashboards in Looker Studio (and basic LookML), apply IAM and encryption best practices, and use built-in ML capabilities (BigQuery ML / AutoML) to deliver actionable insights.

Lessons are project-based: each module includes a mini project (sample datasets provided) so you practice end-to-end—from data acquisition through transformation, analysis, visualization and governance. Frequent quizzes, a full practice exam, and instructor feedback ensure readiness for the Associate Data Practitioner certification. v1.0_associate_data_practitione…

Course structure & module titles

1. Module 1 — Selecting Cloud Storage & Ingestion Solutions (2.5 hours)

   • Overview of Cloud Storage, BigQuery, Cloud SQL, Bigtable, Firestore, Spanner.

   • When to use CSV/JSON/Parquet/Avro.

   • Batch vs streaming ingestion: Storage Transfer Service, Transfer Appliance, Pub/Sub.

   • Lab: Load mixed CSV/JSON datasets into Cloud Storage and import into BigQuery.

2. Module 2 — Data Preparation & Transformation Techniques

   • Data quality checks, schema design, cleaning strategies, common ETL/ELT patterns.

   • Tools: BigQuery SQL, Dataflow, Cloud Data Fusion, Dataform.

   • Performance patterns: partitioning, clustering, denormalization.

     
     

3. Module 3 — Designing & Orchestrating Data Pipelines

   • Pipeline patterns (batch/streaming), orchestration options: Cloud Composer, Cloud Scheduler, Workflows.

   • Monitoring, retries, SLAs, logging and alerting (Cloud Monitoring & Logging).

   • Event-driven ingestion (Pub/Sub → Dataflow → BigQuery).

     
     

4. Module 4 — Analysis & Dashboarding with BigQuery and Looker Studio

   • Writing performant BigQuery SQL queries, analytical functions and windowing.

   • Looker Studio fundamentals and dashboard design best practices; basic LookML concepts.

   • Storytelling with data and stakeholder-focused visualizations.

     
     

5. Module 5 — Data Security, Governance & Lifecycle Management

   • IAM roles & least privilege, dataset and table-level access controls.

   • Encryption options (GMEK, CMEK), data residency, retention policies, Object lifecycle rules.

   • Backups, replication, Analytics Hub sharing patterns.

     
     

6. Module 6 — Integrating Basic ML into Analytics Workflows

   • BigQuery ML basics: training, evaluating, exporting predictions.

   • When to use AutoML or pretrained models; basic model performance metrics.

Learning objectives (titles)

1. Selecting Cloud Storage & Ingestion Solutions

2. Data Preparation & Transformation Techniques

3. Designing & Orchestrating Data Pipelines

4. Analysis & Dashboarding with BigQuery and Looker Studio

5. Data Security, Governance & Lifecycle Management

6. Integrating Basic ML into Analytics Workflows

Prerequisites

• Basic SQL (SELECT, JOINs, GROUP BY).

• Comfortable with spreadsheets and basic statistics.

• Google account (recommended: access to a Google Cloud project).

• Basic web browser and command-line familiarity.

• Recommended: Python familiarity and prior exposure to BigQuery or a Cloud Foundations course.

  
  

Who this course is for

• Aspiring data practitioners preparing for the Google Associate Data Practitioner exam.

• Analysts & engineers who need practical skills for ingestion, transformation, analytics, and governance on Google Cloud.

• Managers who want a grounded understanding of analytics pipelines to partner with technical teams.

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