DBMS > Google BigQuery vs. Ingres vs. MarkLogic vs. Oracle Berkeley DB vs. Sadas Engine

System Properties Comparison Google BigQuery vs. Ingres vs. MarkLogic vs. Oracle Berkeley DB vs. Sadas Engine

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Well established RDBMS

Operational and transactional Enterprise NoSQL database

Widely used in-process key-value store

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

Primary database model

Relational DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	4.11
Rank	#81	Overall
	#44	Relational DBMS

Score	5.92
Rank	#58	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#6	Search engines

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Website

cloud.google.com/bigquery

www.actian.com/databases/ingres

www.marklogic.com

www.oracle.com/database/technologies/related/berkeleydb.html

www.sadasengine.com

Technical documentation

cloud.google.com/bigquery/docs

docs.actian.com/ingres

docs.marklogic.com

docs.oracle.com/cd/E17076_05/html/index.html

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

Developer

Google

Actian Corporation

MarkLogic Corp.

Oracle

originally developed by Sleepycat, which was acquired by Oracle

SADAS s.r.l.

Initial release

2010

1974

originally developed at University Berkely in early 1970s

2001

1994

2006

Current release

11.2, May 2022

11.0, December 2022

18.1.40, May 2020

8.0

License

Commercial or Open Source

commercial

restricted free version is available

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

C++

C, Java, C++ (depending on the Berkeley DB edition)

C++

Server operating systems

hosted

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Linux
OS X
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

AIX
Linux
Windows

Data scheme

yes

schema-free

Schema can be enforced

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.

but tools for importing/exporting data from/to XML-files available

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

yes

SQL92

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

JDBC
ODBC
Proprietary protocol

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

via XQuery or JavaScript

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Ingres Star to access multiple databases simultaneously

Sharding

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Ingres Replicator

yes

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

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

ACID

can act as a resource manager in an XA/JTA transaction

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, with Range Indexes

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)

fine grained access rights according to SQL-standard

Role-based access control at the document and subdocument levels

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

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