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

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.

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.

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.

  
  

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

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

Performance patterns: partitioning, clustering, denormalization.

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).

  
  

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).

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.

  
  

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.

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.

  
  

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.

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.

BigQuery ML basics: training, evaluating, exporting predictions.

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

Learning objectives (titles)

Selecting Cloud Storage & Ingestion Solutions

Data Preparation & Transformation Techniques

Designing & Orchestrating Data Pipelines

Analysis & Dashboarding with BigQuery and Looker Studio

Data Security, Governance & Lifecycle Management

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.

  
  

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.

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.