DBMS > Apache Druid vs. Datastax Enterprise vs. Google BigQuery vs. Linter vs. NuoDB

System Properties Comparison Apache Druid vs. Datastax Enterprise vs. Google BigQuery vs. Linter vs. NuoDB

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

DataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.

Large scale data warehouse service with append-only tables

RDBMS for high security requirements

NuoDB is a webscale distributed database that supports SQL and ACID transactions

Primary database model

Relational DBMS
Time Series DBMS

Wide column store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

Spatial 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	5.80
Rank	#60	Overall
	#4	Wide column stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.09
Rank	#346	Overall
	#152	Relational DBMS

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Website

druid.apache.org

www.datastax.com/products/datastax-enterprise

cloud.google.com/bigquery

linter.ru

www.3ds.com/nuodb-distributed-sql-database

Technical documentation

druid.apache.org/docs/latest/design

docs.datastax.com

cloud.google.com/bigquery/docs

doc.nuodb.com

Developer

Apache Software Foundation and contributors

DataStax

Google

relex.ru

Dassault Systèmes

originally NuoDB, Inc.

Initial release

2012

2011

2010

1990

2013

Current release

29.0.1, April 2024

6.8, April 2020

License

Commercial or Open Source

Open Source

Apache license v2

commercial

limited edition free

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.

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Implementation language

Java

C and C++

C++

Server operating systems

Linux
OS X
Unix

Linux
OS X

hosted

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
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

SQL

Support of SQL

SQL for querying

SQL-like DML and DDL statements (CQL); Spark SQL

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

RESTful HTTP/JSON API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

ADO.NET
JDBC
ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

proprietary syntax with the possibility to convert from PL/SQL

Java, SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

no "single point of failure"

none

data is dynamically stored/cached on the nodes where it is read/written

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

configurable replication factor, datacenter aware, advanced replication for edge computing

Source-replica replication

yes

Managed transparently by NuoDB

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomicity and isolation are supported for single operations

Since BigQuery is designed for querying data

ACID

tunable commit protocol

Concurrency

Support for concurrent manipulation of data

yes

MVCC

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

Temporary table

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 object

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

Standard SQL roles/ privileges, Administrative Users

More information provided by the system vendor

Specific characteristics

DataStax Enterprise is scale-out data infrastructure for enterprises that need to...
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Competitive advantages

Supporting the following application requirements: Zero downtime - Built on Apache...
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Typical application scenarios

Applications that must be massively and linearly scalable with 100% uptime and able...
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Key customers

Capital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
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Market metrics

Among the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
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Licensing and pricing models

Annual subscription
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