DBMS > Google BigQuery vs. Hazelcast vs. NuoDB vs. OceanBase vs. Sadas Engine

System Properties Comparison Google BigQuery vs. Hazelcast vs. NuoDB vs. OceanBase vs. Sadas Engine

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A widely adopted in-memory data grid

NuoDB is a webscale distributed database that supports SQL and ACID transactions

A distributed, high available RDBMS compatible with Oracle and MySQL

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Wide column store

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	0.94
Rank	#197	Overall
	#92	Relational DBMS

Score	1.57
Rank	#149	Overall
	#69	Relational DBMS

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Website

cloud.google.com/bigquery

hazelcast.com

www.3ds.com/nuodb-distributed-sql-database

en.oceanbase.com

www.sadasengine.com

Technical documentation

cloud.google.com/bigquery/docs

hazelcast.org/imdg/docs

doc.nuodb.com

en.oceanbase.com/docs/oceanbase-database

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Google

Hazelcast

Dassault Systèmes

originally NuoDB, Inc.

OceanBase

previously Alibaba and Ant Group

SADAS s.r.l.

Initial release

2010

2008

2013

2010

2006

Current release

5.3.6, November 2023

4.3.0, April 2024

8.0

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

limited edition free

Open Source

Commercial license available

commercial

free trial version available

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

C++

Server operating systems

hosted

All OS with a Java VM

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Linux

AIX
Linux
Windows

Data scheme

yes

schema-free

yes

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

RESTful HTTP/JSON API

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Proprietary native API
Table API

JDBC
ODBC
Proprietary protocol

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

.Net
C
C++
Go
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby

Ada

in MySQL-compatible model
C

in Oracle- and MySQL- compatible models
C++

in Oracle- and MySQL- compatible models
D

in MySQL-compatible model
Delphi

in MySQL-compatible model
Eiffel

in MySQL-compatible model
Erlang

in MySQL-compatible model
Haskell

in MySQL-compatible model
Java

in Oracle- and MySQL- compatible models
JavaScript (Node.js)

in MySQL-compatible model
Objective-C

in MySQL-compatible model
OCaml

in MySQL-compatible model
Perl

in MySQL-compatible model
PHP

in MySQL-compatible model
Python

in MySQL-compatible model
Ruby

in MySQL-compatible model
Scheme

in MySQL-compatible model
Tcl

in MySQL-compatible model

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Event Listeners, Executor Services

Java, SQL

PL/SQL in oracle-compatible mode, MySQL Stored Procedure in mysql-compatible mode

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

data is dynamically stored/cached on the nodes where it is read/written

horizontal partitioning (by hash, key, range, range columns, list, and list columns)

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

yes

Managed transparently by NuoDB

Multi-source replication using Paxos

none

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 selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

one or two-phase-commit; repeatable reads; read commited

ACID

tunable commit protocol

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

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

Temporary table

yes

managed by 'Learn by Usage'

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Role-based access control

Standard SQL roles/ privileges, Administrative Users

Access rights for users, groups and roles according to SQL-standard

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