DBMS > Apache Phoenix vs. Google BigQuery vs. SAP SQL Anywhere vs. SurrealDB

System Properties Comparison Apache Phoenix vs. Google BigQuery vs. SAP SQL Anywhere vs. SurrealDB

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Editorial information provided by DB-Engines

Name

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

Large scale data warehouse service with append-only tables

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

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

Primary database model

Relational DBMS

Document store
Graph DBMS

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

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	3.95
Rank	#80	Overall
	#42	Relational DBMS

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

Website

phoenix.apache.org

cloud.google.com/bigquery

www.sap.com/products/technology-platform/sql-anywhere.html

surrealdb.com

Technical documentation

phoenix.apache.org

cloud.google.com/bigquery/docs

help.sap.com/docs/SAP_SQL_Anywhere

surrealdb.com/docs

Developer

Apache Software Foundation

Google

SAP

formerly Sybase

SurrealDB Ltd

Initial release

2014

2010

1992

2022

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

17, July 2015

v1.5.0, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Rust

Server operating systems

Linux
Unix
Windows

hosted

AIX
HP-UX
Linux
OS X
Solaris
Windows

Linux
macOS
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

yes

schema-free

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

JDBC

RESTful HTTP/JSON API

ADO.NET
HTTP API
JDBC
ODBC

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes, in C/C++, Java, .Net or Perl

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

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 Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

yes, based on authentication and database rules

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