Google Cloud Digital LeaderDigital transformation with Google CloudMedium
A global automotive manufacturer is looking to digitally transform its supply chain operations. They want to implement a solution that provides real-time visibility into the movement of vehicles, parts, and raw materials across their entire global network. This requires ingesting and processing massive amounts of geospatial data from IoT sensors, GPS devices, and logistics partners, and then visualizing this data on interactive maps to optimize routes and predict delivery times. Which Google Cloud service is specifically designed to handle and analyze geospatial data at scale?
- ACloud Logging
- BCloud Storage
- CBigQuery GIS
- DCloud SQL
Show answer & explanationAnswer & explanation
Correct answer: C. BigQuery GIS
BigQuery GIS extends BigQuery's analytical capabilities to support geospatial data types and functions. It enables users to store, analyze, and visualize massive datasets with geographic information, making it perfect for real-time supply chain visibility, route optimization, and predictive analytics based on geospatial data.
Why the other options are wrong
- A. Cloud Logging is for collecting and analyzing logs, not for geospatial data analysis.
- B. Cloud Storage is an object storage service, useful for storing raw geospatial data files, but it does not provide the analytical capabilities to process and query this data spatially.
- D. Cloud SQL is a relational database and while it can store some geospatial data, it is not optimized for large-scale geospatial analytics like BigQuery GIS.
BigQuery GIS
An extension of BigQuery that allows for storing, analyzing, and visualizing geospatial data using standard SQL.
- Integrates geospatial data with BigQuery's analytical power
- Supports standard GIS functions and data types
- Scalable for massive geospatial datasets
Memory trick: BigQuery GIS: Big data, big maps, big insights.