DBMS > Apache Druid vs. Google BigQuery vs. Microsoft Azure AI Search vs. Quasardb vs. SiteWhere

System Properties Comparison Apache Druid vs. Google BigQuery vs. Microsoft Azure AI Search vs. Quasardb vs. SiteWhere

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

Large scale data warehouse service with append-only tables

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

Distributed, high-performance timeseries database

M2M integration platform for persisting/querying time series data

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Search engine

Time Series DBMS

Secondary database models

Vector DBMS

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	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

druid.apache.org

cloud.google.com/bigquery

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

quasar.ai

github.com/sitewhere/sitewhere

Technical documentation

druid.apache.org/docs/latest/design

cloud.google.com/bigquery/docs

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

doc.quasar.ai/master

sitewhere1.sitewhere.io/index.html

Developer

Apache Software Foundation and contributors

Google

Microsoft

quasardb

SiteWhere

Initial release

2012

2010

2015

2009

2010

Current release

29.0.1, April 2024

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Common Public Attribution License Version 1.0

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

Server operating systems

Linux
OS X
Unix

hosted

BSD
Linux
OS X
Windows

Linux
OS X
Windows

Data scheme

yes

schema-less columns are supported

yes

schema-free

predefined scheme

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

SQL

Support of SQL

SQL for querying

yes

SQL-like query language

APIs and other access methods

JDBC
RESTful HTTP/JSON API

RESTful HTTP/JSON API

RESTful HTTP API

HTTP API

HTTP REST

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
Java
JavaScript
Python

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

Sharding

Implicit feature of the cloud service

Sharding

consistent hashing

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes

Implicit feature of the cloud service

Source-replica replication with selectable replication factor

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

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

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

Google Cloud Identity & Access Management (IAM)

yes

using Azure authentication

Cryptographically strong user authentication and audit trail

Users with fine-grained authorization concept

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