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Google BigQuery vs PostGIS: What are the differences?
Introduction
Google BigQuery and PostGIS are two popular database systems used for analyzing and managing spatial data. While both systems offer capabilities for working with geospatial data, there are several key differences between the two.
Data Storage: Google BigQuery is a cloud-based service that stores data in a distributed manner, making it highly scalable and suitable for handling large datasets. In contrast, PostGIS is an extension of the PostgreSQL database, storing data in a traditional relational manner.
Spatial Indexing: PostGIS has built-in support for spatial indexing using various indexing methods like R-tree, GiST, and B-tree. This allows for efficient spatial queries and operations on large datasets. On the other hand, Google BigQuery does not provide native support for spatial indexing, making spatial queries potentially slower and less efficient.
SQL Functions: PostGIS offers a rich set of SQL functions specifically designed for spatial data processing. These functions include distance calculations, overlays, spatial joins, and more. While Google BigQuery also supports SQL, its spatial function library is more limited compared to PostGIS.
Data Import and Export: Google BigQuery supports easy integration with other Google Cloud services, allowing for seamless data import and export from various sources like Cloud Storage, Cloud Pub/Sub, and more. PostGIS, being a PostgreSQL extension, offers a wide range of import and export options but might require additional configuration and setup.
Data Visualization: Google BigQuery provides built-in data visualization capabilities, allowing users to create charts, graphs, and dashboards directly within the platform. PostGIS, being a database management system, does not have built-in visualization tools, and users need to use external tools or applications for data visualization.
Cost Structure: Google BigQuery follows a pay-per-use pricing model, where users are charged based on the amount of data processed and the number of queries run. In contrast, PostGIS is typically deployed on self-managed servers, with costs associated with hardware, maintenance, and administration.
In Summary, Google BigQuery is a cloud-based, scalable database service with limited spatial functions and built-in data visualization capabilities. PostGIS is an extension of PostgreSQL, offering rich spatial functionality with native spatial indexing, numerous SQL functions, and flexible data import/export options.
Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.
Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.
BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.
BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.
Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.
BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.
We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution
Pros of Google BigQuery
- High Performance28
- Easy to use25
- Fully managed service22
- Cheap Pricing19
- Process hundreds of GB in seconds16
- Big Data12
- Full table scans in seconds, no indexes needed11
- Always on, no per-hour costs8
- Good combination with fluentd6
- Machine learning4
- Easy to manage1
- Easy to learn0
Pros of PostGIS
- De facto GIS in SQL25
- Good Documentation5
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Cons of Google BigQuery
- You can't unit test changes in BQ data1
- Sdas0
Cons of PostGIS
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What is Google BigQuery?
What is PostGIS?
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