DBMS > Apache Druid vs. CouchDB vs. Google BigQuery vs. Microsoft Azure AI Search vs. SAP IQ

System Properties Comparison Apache Druid vs. CouchDB vs. Google BigQuery vs. Microsoft Azure AI Search vs. SAP IQ

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

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

Large scale data warehouse service with append-only tables

Search-as-a-service for web and mobile app development

Columnar RDBMS optimized for Big Data analytics

Primary database model

Relational DBMS
Time Series DBMS

column-oriented

Secondary database models

Spatial DBMS

using the Geocouch extension

Vector DBMS

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

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	5.52
Rank	#59	Overall
	#6	Search engines

Score	2.64
Rank	#108	Overall
	#52	Relational DBMS

Website

druid.apache.org

couchdb.apache.org

cloud.google.com/bigquery

azure.microsoft.com/en-us/services/search

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

Technical documentation

druid.apache.org/docs/latest/design

docs.couchdb.org/en/stable

cloud.google.com/bigquery/docs

learn.microsoft.com/en-us/azure/search

help.sap.com/docs/SAP_IQ

Developer

Apache Software Foundation and contributors

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Google

Microsoft

SAP, formerly Sybase

Initial release

2012

2005

2010

2015

1994

Current release

29.0.1, April 2024

3.3.3, December 2023

16.1 SPS04, April 2019

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache version 2

commercial

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

Erlang

Server operating systems

Linux
OS X
Unix

Android
BSD
Linux
OS X
Solaris
Windows

hosted

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

schema-less columns are supported

schema-free

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.

Secondary indexes

yes

via views

yes

SQL

Support of SQL

SQL for querying

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

RESTful HTTP/JSON API

RESTful HTTP API

ADO.NET
JDBC
ODBC
RESTful HTTP API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
Java
JavaScript
Python

C
C#
C++
Java
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

View functions in JavaScript

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

improved architecture with release 2.0

none

Sharding

Implicit feature of the cloud service

shared disk or shared nothing architectures with SAP IQ Multiplexer

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

SAP/Sybase Replication Server

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

atomic operations within a single document possible

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

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.

User concepts

Access control

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

Access rights for users can be defined per database

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

Google Cloud Identity & Access Management (IAM)

yes

using Azure authentication

fine grained access rights according to SQL-standard

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