What Cloud data warehouse is best for geospatial processing?
Spatial science teams need a data warehouse that can support the sheer volume of geospatial data they have to process.
There are now a number of Cloud data warehouse options available in the market and this has created a new challenge for organisations; how does a GIS team decide which data warehouse will meet the storage and processing needs of their geospatial data while staying cost-effective?
Two of the major players in this space are Google Cloud’s BigQuery and Snowflake’s data warehouse.
BigQuery is a serverless Cloud data warehouse that is highly scalable, cost-effective and can analyse large and complex datasets and respond within seconds.
Snowflake is a SQL (Structured Query Language) data warehouse built for the Cloud. Snowflake’s unique architecture natively handles diverse data in a single system, with the elasticity to support and scale data, workload and users.
To help make this decision easier for your team, we ran comparative testing on a BigQuery instance and Snowflake’s extra small and large instances to see how they stacked up against each other in:
- Data Loading and Unloading
- Data Consistency
- Programmability and Integration
- Performance and Pricing
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