Description

Introduction

Loading and exporting data efficiently is a key part of modern cloud data warehousing. This training focuses on Google BigQuery, a serverless platform offered by Google Cloud Platform.Learners will explore both batch and streaming methods for data ingestion and extraction. In addition, they will understand how to handle large-scale data operations. Moreover, the course covers real-time analytics and high-performance workflows.

Because of this approach, participants gain practical knowledge of building data pipelines. Therefore, the training emphasizes implementation, architecture, and optimization for enterprise use.

Learner Prerequisites

• Basic understanding of SQL and relational databases
• Familiarity with cloud computing concepts
• Awareness of data warehousing fundamentals
• Basic knowledge of JSON/CSV file formats
• Understanding of APIs and data pipelines (optional but helpful)

Table of Contents

1. Data Loading Fundamentals in BigQuery
1.1 Overview of batch vs streaming ingestion
1.2 Supported data formats (CSV, JSON, Avro, Parquet)
1.3 Load job configurations and parameters
1.4 Schema detection and manual schema mapping
1.5 Handling load errors and validation rules

2. Batch Data Loading Techniques
2.1 Loading data from Google Cloud Storage
2.2 Uploading local files into datasets
2.3 Managing partitioned and clustered tables during load
2.4 Append vs overwrite strategies for datasets
2.5 Optimizing batch load performance and cost

3. Streaming Data Ingestion
3.1 Introduction to streaming inserts in BigQuery
3.2 Using the Storage Write API for real-time ingestion
3.3 Designing real-time streaming architectures
3.4 Handling duplicates and ensuring idempotency
3.5 Managing latency and throughput trade-offs

4. Exporting Data from BigQuery
4.1 Exporting data to Google Cloud Storage
4.2 Supported export formats and configurations
4.3 Partition-wise and filtered exports
4.4 Automating scheduled export jobs
4.5 Data retention and lifecycle management strategies

5. Automation & Integration of Data Pipelines
5.1 Workflow orchestration using Cloud Composer
5.2 Using BigQuery REST APIs and client libraries
5.3 Integration with Dataflow for ETL pipelines
5.4 CI/CD practices for data pipeline deployment
5.5 Monitoring and alerting pipeline executions

6. Monitoring, Security & Best Practices
6.1 Monitoring jobs, queries, and logs in BigQuery
6.2 IAM roles and access control for datasets
6.3 Cost optimization strategies for storage and queries
6.4 Data governance and compliance practices
6.5 Troubleshooting common ingestion and export issues

Conclusion

This training builds strong expertise in loading and exporting data using Google BigQuery. It covers both batch and streaming approaches.

In addition, learners understand how to design ingestion pipelines and export strategies. They also explore automation techniques for efficient workflows. Moreover, the course highlights security and cost optimization practices.As a result, participants can develop scalable and reliable data pipelines. Therefore, they will be able to manage real-time analytics and enterprise data workflows effectively on Google Cloud Platform.