DBMS > Google BigQuery vs. Kyligence Enterprise vs. MarkLogic vs. Splice Machine vs. SwayDB

System Properties Comparison Google BigQuery vs. Kyligence Enterprise vs. MarkLogic vs. Splice Machine vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A distributed analytics engine for big data, built on top of Apache Kylin

Operational and transactional Enterprise NoSQL database

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Relational DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational DBMS

Key-value store

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.40
Rank	#269	Overall
	#124	Relational DBMS

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

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

cloud.google.com/bigquery

kyligence.io/kyligence-enterprise

www.marklogic.com

splicemachine.com

swaydb.simer.au

Technical documentation

cloud.google.com/bigquery/docs

docs.marklogic.com

splicemachine.com/how-it-works

Developer

Google

Kyligence, Inc.

MarkLogic Corp.

Splice Machine

Simer Plaha

Initial release

2010

2016

2001

2014

2018

Current release

11.0, December 2022

3.1, March 2021

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

AGPL 3.0, commercial license available

Open Source

GNU Affero GPL V3.0

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++

Java

Scala

Server operating systems

hosted

Linux

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

Schema can be enforced

yes

schema-free

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

SQL

Support of SQL

yes

ANSI SQL for queries (using Apache Calcite)

yes

SQL92

yes

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
RESTful 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
Native Spark Datasource
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Java
Kotlin
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

via XQuery or JavaScript

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
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

Yes, via Full Spark Integration

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

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (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

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

Google Cloud Identity & Access Management (IAM)

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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