Description

Introduction

This training focuses on building data pipelines using Google Cloud Platform and Google BigQuery. It covers the complete process of designing and managing modern data workflows. Learners explore data ingestion, transformation, and orchestration techniques. In addition, they learn how to optimize pipelines for performance and cost. Moreover, the course highlights governance and monitoring practices.

As a result, participants can build scalable and production-ready data pipelines. Therefore, this training supports real-world cloud data engineering use cases.

Learner Prerequisites

• Basic understanding of SQL and relational databases
• Familiarity with data warehousing concepts
• Introductory knowledge of cloud computing concepts
• Awareness of ETL/ELT processes
• Basic understanding of APIs and data formats (JSON, CSV)

Table of Contents

1. Introduction to Data Pipelines on Google Cloud Platform
1.1 Overview of modern data pipelines
1.2 Batch vs streaming pipeline architecture
1.3 Key GCP services for data engineering
1.4 End-to-end pipeline lifecycle

2. BigQuery Fundamentals for Data Engineering
2.1 BigQuery architecture and processing model
2.2 Datasets, tables, and partitions
2.3 Storage and compute separation
2.4 Query execution and performance basics

3. Data Ingestion into BigQuery (Batch & Streaming)
3.1 Loading data from Cloud Storage
3.2 Streaming inserts and real-time ingestion
3.3 Using Pub/Sub for event-driven ingestion
3.4 Data validation during ingestion

4. Data Transformation and Processing Layer
4.1 SQL-based transformations in BigQuery
4.2 Building ELT workflows
4.3 Using Dataform for transformation pipelines
4.4 Handling structured and semi-structured data

5. Workflow Orchestration and Scheduling
5.1 Introduction to Cloud Composer
5.2 DAG design for data pipelines
5.3 Scheduling and dependency management
5.4 Error handling and retries

6. Pipeline Optimization and Performance Tuning
6.1 Partitioning and clustering strategies
6.2 Query optimization techniques
6.3 Cost control and resource management
6.4 Monitoring job performance

7. Data Quality, Security, and Governance
7.1 IAM roles and access control
7.2 Data validation and quality checks
7.3 Audit logging and monitoring
7.4 Governance best practices

Conclusion

This training provides a practical approach to building data pipelines using Google Cloud Platform and Google BigQuery. It covers both batch and real-time processing. In addition, learners understand how to design scalable workflows. They also learn how to ensure data quality and security. Moreover, the course focuses on performance tuning and cost optimization.

As a result, participants can implement efficient and reliable data pipelines. Therefore, they will be able to support modern data-driven applications with confidence.