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- Expires on: 2025/10/15
- Last Update: October 12, 2025
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Price:
69.99 $0 $
About This Course
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.
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.
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).
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.
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.
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)
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.
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.
