Google BigQuery vs PostGIS: What are the differences?
What is Google BigQuery? Analyze terabytes of data in seconds. Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python..
What is PostGIS? Open source spatial database. PostGIS is a spatial database extender for PostgreSQL object-relational database. It adds support for geographic objects allowing location queries to be run in SQL.
Google BigQuery can be classified as a tool in the "Big Data as a Service" category, while PostGIS is grouped under "Database Tools".
Some of the features offered by Google BigQuery are:
- All behind the scenes- Your queries can execute asynchronously in the background, and can be polled for status.
- Import data with ease- Bulk load your data using Google Cloud Storage or stream it in bursts of up to 1,000 rows per second.
- Affordable big data- The first Terabyte of data processed each month is free.
On the other hand, PostGIS provides the following key features:
- Processing and analytic functions for both vector and raster data for splicing, dicing, morphing, reclassifying, and collecting/unioning with the power of SQL
- raster map algebra for fine-grained raster processing
- Spatial reprojection SQL callable functions for both vector and raster data
"High Performance" is the top reason why over 17 developers like Google BigQuery, while over 22 developers mention "De facto GIS in SQL" as the leading cause for choosing PostGIS.
PostGIS is an open source tool with 645 GitHub stars and 246 GitHub forks. Here's a link to PostGIS's open source repository on GitHub.
According to the StackShare community, Google BigQuery has a broader approval, being mentioned in 160 company stacks & 41 developers stacks; compared to PostGIS, which is listed in 53 company stacks and 14 developer stacks.
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I use Google BigQuery because it makes is super easy to query and store data for analytics workloads. If you're using GCP, you're likely using BigQuery. However, running data viz tools directly connected to BigQuery will run pretty slow. They recently announced BI Engine which will hopefully compete well against big players like Snowflake when it comes to concurrency.
What's nice too is that it has SQL-based ML tools, and it has great GIS support!
BigQuery allows our team to pull reports quickly using a SQL-like queries against our large store of data about social sharing. We use the information throughout the company, to do everything from making internal product decisions based on usage patterns to sharing certain kinds of custom reports with our publishers.
Aggregation of user events and traits across a marketing website, SaaS web application, user account provisioning backend and Salesforce CRM. Enables full-funnel analysis of campaign ROI, customer acquisition, engagement and retention at both the user and target account level.
Google's insanely fast, feature-rich, zero-maintenance column store. Used for real-time customer data queries.
PostGIS makes it easy (and fast) to do geographic queries, such as nearest-neighbor and bounding box queries.
Backend for weather forecast data that Geoserver queries to build updated weather maps