Google BigQuery vs PostGIS

Overview

Google BigQuery

Stacks1.8K

Followers1.5K

Votes152

PostGIS

Stacks381

Followers377

Votes30

GitHub Stars2.0K

Forks407

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.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Google BigQuery, PostGIS

Julien

CTO at Hawk

Sep 19, 2020

Decided

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

193k views193k

Comments

Detailed Comparison

Google BigQuery	PostGIS
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.	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.
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.;The right interface- Separate interfaces for administration and developers will make sure that you have access to the tools you need.	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;Support for importing / exporting ESRI shapefile vector data via both commandline and GUI packaged tools and support for more formats via other 3rd-party Open Source tools
Statistics
GitHub Stars -	GitHub Stars 2.0K
GitHub Forks -	GitHub Forks 407
Stacks 1.8K	Stacks 381
Followers 1.5K	Followers 377
Votes 152	Votes 30
Pros & Cons
Pros 28 High Performance 25 Easy to use 22 Fully managed service 19 Cheap Pricing 16 Process hundreds of GB in seconds Cons 1 You can't unit test changes in BQ data 0 Sdas	Pros 25 De facto GIS in SQL 5 Good Documentation
Integrations
Xplenty Fluentd Looker Chartio Treasure Data	PostgreSQL

What are some alternatives to Google BigQuery, PostGIS?

dbForge Studio for MySQL

It is the universal MySQL and MariaDB client for database management, administration and development. With the help of this intelligent MySQL client the work with data and code has become easier and more convenient. This tool provides utilities to compare, synchronize, and backup MySQL databases with scheduling, and gives possibility to analyze and report MySQL tables data.

dbForge Studio for Oracle

It is a powerful integrated development environment (IDE) which helps Oracle SQL developers to increase PL/SQL coding speed, provides versatile data editing tools for managing in-database and external data.

dbForge Studio for PostgreSQL

It is a GUI tool for database development and management. The IDE for PostgreSQL allows users to create, develop, and execute queries, edit and adjust the code to their requirements in a convenient and user-friendly interface.

dbForge Studio for SQL Server

It is a powerful IDE for SQL Server management, administration, development, data reporting and analysis. The tool will help SQL developers to manage databases, version-control database changes in popular source control systems, speed up routine tasks, as well, as to make complex database changes.

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Liquibase

Liquibase is th leading open-source tool for database schema change management. Liquibase helps teams track, version, and deploy database schema and logic changes so they can automate their database code process with their app code process.

Sequel Pro

Sequel Pro is a fast, easy-to-use Mac database management application for working with MySQL databases.

DBeaver

It is a free multi-platform database tool for developers, SQL programmers, database administrators and analysts. Supports all popular databases: MySQL, PostgreSQL, SQLite, Oracle, DB2, SQL Server, Sybase, Teradata, MongoDB, Cassandra, Redis, etc.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

dbForge SQL Complete

It is an IntelliSense add-in for SQL Server Management Studio, designed to provide the fastest T-SQL query typing ever possible.

Related Comparisons

Google BigQuery vs PostGIS: What are the differences?

Introduction

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.

Google BigQuery vs PostGIS

Overview