What you'll learn:
- Understand the core services and tools used in Google Cloud for data management, analytics, and orchestration
- Design and implement data pipelines using BigQuery, Cloud Composer, Dataflow, Dataform, and Dataproc
- Perform data preparation, transformation, and ingestion using Cloud Data Fusion and BigQuery
- Analyze and visualize data using BigQuery, Looker Studio, and BigQuery ML
- Understand the differences and use cases of data storage options like BigQuery, Cloud Storage, Firestore, Cloud SQL, Bigtable, and Spanner
- Apply ETL, ELT, and ETLT concepts in real-world cloud data workflows
- Build, schedule, and monitor data workflows using Cloud Composer (Apache Airflow)
- Gain hands-on experience through labs aligned with the official certification exam guide
- Prepare effectively for the Google Cloud Associate Data Practitioner certification exam
This course is a comprehensive, hands-on learning path designed to help you prepare for the Google Cloud Associate Data Practitioner Certification, following the structure and objectives outlined in the official exam guide.
The certification targets individuals working with data in the cloud, requiring foundational skills in managing, processing, analyzing, and visualizing data using Google Cloud technologies.
In this course, you’ll learn to confidently work across various GCP services and develop a clear understanding of their practical use in end-to-end data workflows.
Key Focus Areas:
Data Preparation and Ingestion: Learn to differentiate between ETL, ELT, and ETLT, clean and transform datasets, and work with tools like Cloud Data Fusion and BigQuery.
Data Analysis and Visualization: Use BigQuery to explore datasets, interpret analytical results, and build impactful dashboards with Looker Studio. Learn to utilize BigQuery ML and AutoML for predictive insights.
Data Pipeline Orchestration: Implement and schedule data pipelines using Cloud Composer (Apache Airflow), Dataflow (Apache Beam), Dataform, and Dataproc.
Data Management: Understand when to use Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner, and AlloyDB, including considerations around cost, scale, and performance.
This course blends theory with practical labs, real-world scenarios, and project-based exercises to help you internalize concepts and gain confidence.
Whether you're aiming to clear the exam or build a strong data foundation in GCP, this course provides everything you need to succeed.
