DBMS > BigObject vs. HEAVY.AI vs. MarkLogic vs. Riak KV vs. Snowflake

System Properties Comparison BigObject vs. HEAVY.AI vs. MarkLogic vs. Riak KV vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Operational and transactional Enterprise NoSQL database

Distributed, fault tolerant key-value store

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.13
Rank	#329	Overall
	#147	Relational DBMS

Score	1.41
Rank	#153	Overall
	#71	Relational DBMS

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

Score	3.84
Rank	#76	Overall
	#9	Key-value stores

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Website

bigobject.io

github.com/heavyai/heavydb
www.heavy.ai

www.progress.com/marklogic

www.snowflake.com

Technical documentation

docs.bigobject.io

docs.heavy.ai

www.progress.com/marklogic/documentation

www.tiot.jp/riak-docs/riak/kv/latest

docs.snowflake.net/manuals/index.html

Developer

BigObject, Inc.

HEAVY.AI, Inc.

MarkLogic Corp.

OpenSource, formerly Basho Technologies

Snowflake Computing Inc.

Initial release

2015

2016

2001

2009

2014

Current release

5.10, January 2022

11.0, December 2022

3.2.0, December 2022

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache Version 2; enterprise edition available

commercial

restricted free version is available

Open Source

Apache version 2, commercial enterprise edition

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++ and CUDA

C++

Erlang

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux

Linux
OS X
Windows

Linux
OS X

hosted

Data scheme

yes

schema-free

Schema can be enforced

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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

restricted

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL92

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

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

HTTP API
Native Erlang Interface

CLI Client
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Lua

yes

via XQuery or JavaScript

Erlang

user defined functions

Triggers

yes

pre-commit hooks and post-commit hooks

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

Sharding

no "single point of failure"

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

yes

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

links between data sets can be stored

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

Read/write lock on objects (tables, trees)

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

yes, with Range Indexes

User concepts

Access control

fine grained access rights according to SQL-standard

Role-based access control at the document and subdocument levels

yes, using Riak Security

Users with fine-grained authorization concept, user roles and pluggable authentication

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.