About Course
Google Cloud Data Engineer
This course focuses on designing, building, and managing data processing systems on
Google Cloud Platform (GCP). Participants learn how to create reliable
data pipelines, manage large-scale datasets, and enable analytics-driven decision making
using Google Cloud services. The training emphasizes real-world data engineering use cases
and best practices.
Prerequisites
- Basic understanding of cloud computing concepts
- Familiarity with databases and data processing fundamentals
- Working knowledge of SQL and data querying concepts
- General awareness of programming or scripting languages
- Prior exposure to analytics platforms is helpful but not mandatory
Who Should Attend
- Data Engineers and Data Analysts
- Cloud Engineers working with data platforms
- Software Engineers handling data-intensive applications
- BI professionals transitioning to cloud-based analytics
- Professionals preparing for the Google Cloud Data Engineer certification
Key Skills You Will Gain
This course equips learners with practical skills required to build and manage
scalable data solutions on Google Cloud.
- Designing and managing data processing pipelines
- Working with structured and unstructured datasets
- Implementing data storage and analytics solutions
- Ensuring data security, reliability, and quality
- Optimizing data workflows for performance and cost
Course Modules
Module 1: Data Engineering Fundamentals
- Overview of data engineering on Google Cloud
- Data lifecycle and architecture principles
- Choosing the right data processing approach
Module 2: Data Storage and Management
- Cloud Storage, BigQuery, and Cloud SQL
- Designing scalable data storage solutions
- Managing data schemas and access
Module 3: Data Processing and Pipelines
- Batch and streaming data pipelines
- Using Dataflow and Pub/Sub
- Building reliable and scalable pipelines
Module 4: Analytics and Machine Learning Integration
- Querying and analyzing data with BigQuery
- Integrating analytics with downstream applications
- Supporting ML workloads with data pipelines
Module 5: Security, Monitoring, and Optimization
- Securing data and managing access
- Monitoring data pipelines and workflows
- Optimizing performance and controlling costs