• Understanding modern data warehousing concepts
• Differences between traditional and cloud-based warehouses
• Overview of serverless architecture
• Importance of scalable analytics platforms
• Real-world applications of cloud data warehouses
• Differences between traditional and cloud-based warehouses
• Overview of serverless architecture
• Importance of scalable analytics platforms
• Real-world applications of cloud data warehouses
• Introduction to Google Cloud ecosystem
• Core services related to data and analytics
• Understanding cloud storage and compute
• Identity and access management basics
• Setting up GCP environment and projects
• Core services related to data and analytics
• Understanding cloud storage and compute
• Identity and access management basics
• Setting up GCP environment and projects
• Introduction to Google BigQuery
• Key features and architecture
• Storage and compute separation
• Serverless and scalable design
• BigQuery use cases in enterprise analytics
• Key features and architecture
• Storage and compute separation
• Serverless and scalable design
• BigQuery use cases in enterprise analytics
• Importing data from various sources
• Batch and streaming data ingestion
• Working with Cloud Storage and APIs
• Managing structured and semi-structured data
• Best practices for data pipelines
• Batch and streaming data ingestion
• Working with Cloud Storage and APIs
• Managing structured and semi-structured data
• Best practices for data pipelines
• Writing queries using standard SQL
• Data filtering, aggregation, and joins
• Query optimization techniques
• Working with nested and repeated data
• Performance tuning for large datasets
• Data filtering, aggregation, and joins
• Query optimization techniques
• Working with nested and repeated data
• Performance tuning for large datasets
• Data preparation and transformation
• Creating views and materialized views
• Data partitioning and clustering
• Designing analytical data models
• Managing large-scale data efficiently
• Creating views and materialized views
• Data partitioning and clustering
• Designing analytical data models
• Managing large-scale data efficiently
• Monitoring query performance
• Cost control and pricing strategies
• Query caching and execution planning
• Resource and workload management
• Best practices for efficient analytics
• Cost control and pricing strategies
• Query caching and execution planning
• Resource and workload management
• Best practices for efficient analytics
