DBMS > Google BigQuery vs. HBase vs. SurrealDB

System Properties Comparison Google BigQuery vs. HBase vs. SurrealDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Wide-column store based on Apache Hadoop and on concepts of BigTable

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Relational DBMS

Wide column store

Document store
Graph DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	1.02
Rank	#190	Overall
	#33	Document stores
	#18	Graph DBMS

Website

cloud.google.com/bigquery

hbase.apache.org

surrealdb.com

Technical documentation

cloud.google.com/bigquery/docs

hbase.apache.org/book.html

surrealdb.com/docs

Developer

Google

Apache Software Foundation

Apache top-level project, originally developed by Powerset

SurrealDB Ltd

Initial release

2010

2008

2022

Current release

2.3.4, January 2021

v1.5.0, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Rust

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

Linux
macOS
Windows

Data scheme

yes

schema-free, schema definition possible

schema-free

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

RESTful HTTP/JSON API

Java API
RESTful HTTP API
Thrift

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Coprocessors in Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Since BigQuery is designed for querying data

Single row ACID (across millions of columns)

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

yes, based on authentication and database rules

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