DBMS > Apache Druid vs. BaseX vs. Google BigQuery vs. SAP Adaptive Server vs. SwayDB

System Properties Comparison Apache Druid vs. BaseX vs. Google BigQuery vs. SAP Adaptive Server vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

Large scale data warehouse service with append-only tables

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

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

Primary database model

Relational DBMS
Time Series DBMS

Native XML DBMS

Relational DBMS

Key-value store

Secondary database models

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	1.73
Rank	#142	Overall
	#4	Native XML DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	36.31
Rank	#25	Overall
	#17	Relational DBMS

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

Website

druid.apache.org

basex.org

cloud.google.com/bigquery

www.sap.com/products/technology-platform/sybase-ase.html

swaydb.simer.au

Technical documentation

druid.apache.org/docs/latest/design

docs.basex.org

cloud.google.com/bigquery/docs

help.sap.com/docs/SAP_ASE

Developer

Apache Software Foundation and contributors

BaseX GmbH

Google

SAP, Sybase

Simer Plaha

Initial release

2012

2007

2010

1987

2018

Current release

29.0.1, April 2024

10.7, August 2023

16.0

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

BSD license

commercial

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

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Implementation language

Java

C and C++

Scala

Server operating systems

Linux
OS X
Unix

Linux
OS X
Windows

hosted

AIX
HP-UX
Linux
Solaris
Unix
Windows

Data scheme

yes

schema-less columns are supported

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

XQuery supports 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.

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Cobol
Java
Java
Perl
PHP
Python

Java
Kotlin
Scala

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

Java and Transact-SQL

Triggers

yes

via events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

none

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

multiple readers, single writer

Since BigQuery is designed for querying data

ACID

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

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

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Users with fine-grained authorization concept on 4 levels

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

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