Can I use this skill to restore deleted data?

Yes, it provides specific commands for BigQuery Time Travel and Snapshots, allowing you to query or restore data from any point within the last 7 days.

What are the limitations of BigQuery clustering columns?

BigQuery supports up to 4 clustering columns. This skill provides best practices on column selection and ordering to maximize query performance.

How does this skill help reduce BigQuery costs?

The skill provides patterns for implementing partitioning and clustering, which prune unnecessary data during scans, significantly lowering the cost of query execution.

Does it support external tables in Google Cloud Storage?

Yes, it includes documentation and commands for creating external tables for CSV, Parquet, and JSON formats stored in GCS.

How do I implement column-level security with this skill?

The skill covers using Data Catalog policy tags within SQL DDL to restrict access to sensitive columns based on IAM roles.

GCP BigQuery Table Management

Name: GCP BigQuery Table Management
Author: FunnelEnvy

byFunnelEnvy

Gestión de Bases de Datos

Manages Google Cloud BigQuery table lifecycles, including creation, partitioning, clustering, and security configurations.

Acerca de

This skill provides specialized guidance for architecting and managing Google Cloud BigQuery tables to ensure optimal performance and cost-efficiency. It covers essential operations such as implementing time-based and range partitioning, defining clustering strategies for high-cardinality data, and managing complex schemas via DDL or CLI commands. Additionally, it handles advanced administrative tasks like creating table snapshots, configuring row-level and column-level security policies, and utilizing time travel for data recovery, making it an indispensable tool for data engineers and cloud architects seeking to automate their data warehouse operations.

Características Principales

0 GitHub stars
Advanced partitioning and clustering strategy implementation for query optimization
Data protection and recovery via snapshots, clones, and time travel
Comprehensive security configuration with row-level and column-level access controls
Automated BigQuery table creation using SQL DDL or BQ CLI commands
Schema evolution management including adding columns and relaxing types

Casos de Uso

Configuring automated data retention and expiration for temporary staging environments
Optimizing query performance and reducing scan costs for multi-terabyte datasets
Implementing fine-grained security policies for sensitive PII data compliance

Acerca de

Características Principales

0 GitHub stars
Advanced partitioning and clustering strategy implementation for query optimization
Data protection and recovery via snapshots, clones, and time travel
Comprehensive security configuration with row-level and column-level access controls
Automated BigQuery table creation using SQL DDL or BQ CLI commands
Schema evolution management including adding columns and relaxing types

Casos de Uso

Configuring automated data retention and expiration for temporary staging environments
Optimizing query performance and reducing scan costs for multi-terabyte datasets
Implementing fine-grained security policies for sensitive PII data compliance