DBMS > Amazon Redshift vs. BigchainDB vs. Heroic vs. MarkLogic vs. TimesTen

System Properties Comparison Amazon Redshift vs. BigchainDB vs. Heroic vs. MarkLogic vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Operational and transactional Enterprise NoSQL database

In-Memory RDBMS compatible to Oracle

Primary database model

Relational DBMS

Document store

Time Series DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

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

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

aws.amazon.com/redshift

www.bigchaindb.com

github.com/spotify/heroic

www.marklogic.com

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

Technical documentation

docs.aws.amazon.com/redshift

bigchaindb.readthedocs.io/en/latest

spotify.github.io/heroic

docs.marklogic.com

docs.oracle.com/database/timesten-18.1

Developer

Amazon (based on PostgreSQL)

Spotify

MarkLogic Corp.

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2012

2016

2014

2001

1998

Current release

11.0, December 2022

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Open Source

AGPL v3

Open Source

Apache 2.0

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

Python

Java

C++

Server operating systems

hosted

Linux

Linux
OS X
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

schema-free

Schema can be enforced

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

Secondary indexes

restricted

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL92

yes

APIs and other access methods

JDBC
ODBC

CLI Client
RESTful HTTP API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

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
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

All languages supporting JDBC/ODBC

Go
Haskell
Java
JavaScript
Python
Ruby

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

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

user defined functions

in Python

yes

via XQuery or JavaScript

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

yes

by means of logfiles and checkpoints

In-memory capabilities

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

yes

yes, with Range Indexes

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes

Role-based access control at the document and subdocument levels

fine grained access rights according to SQL-standard

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