DBMS > Ingres vs. MarkLogic vs. Microsoft Azure Cosmos DB vs. Teradata

System Properties Comparison Ingres vs. MarkLogic vs. Microsoft Azure Cosmos DB vs. Teradata

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Editorial information provided by DB-Engines

Name

Description

Well established RDBMS

Operational and transactional Enterprise NoSQL database

Globally distributed, horizontally scalable, multi-model database service

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Relational DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	3.63
Rank	#79	Overall
	#43	Relational DBMS

Score	4.15
Rank	#70	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#8	Search engines

Score	24.97
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	41.47
Rank	#22	Overall
	#15	Relational DBMS

Website

www.actian.com/databases/ingres

www.progress.com/marklogic

azure.microsoft.com/services/cosmos-db

www.teradata.com

Technical documentation

docs.actian.com/ingres

www.progress.com/marklogic/documentation

learn.microsoft.com/azure/cosmos-db

docs.teradata.com

Developer

Actian Corporation

MarkLogic Corp.

Microsoft

Teradata

Initial release

1974

originally developed at University Berkely in early 1970s

2001

2014

1984

Current release

12.0, July 2024

11.0, December 2022

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

restricted free version is available

commercial

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++

Server operating systems

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Linux
OS X
Windows

hosted

hosted
Linux

Data scheme

yes

schema-free

Schema can be enforced

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

Secondary indexes

yes

All properties auto-indexed by default

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

yes

SQL92

SQL-like query language

yes

SQL 2016 + extensions

APIs and other access methods

.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

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

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

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

JavaScript

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Ingres Star to access multiple databases simultaneously

Sharding

Implicit feature of the cloud service

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Ingres Replicator

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

Multi-item ACID transactions with snapshot isolation within a partition

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

User concepts

Access control

fine grained access rights according to SQL-standard

Role-based access control at the document and subdocument levels

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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