DBMS > Apache Druid vs. Faircom DB vs. Hive vs. NuoDB vs. Snowflake

System Properties Comparison Apache Druid vs. Faircom DB vs. Hive vs. NuoDB vs. Snowflake

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

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Relational DBMS
Time Series DBMS

Key-value store
Relational DBMS

Relational 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	0.29
Rank	#304	Overall
	#43	Key-value stores
	#136	Relational DBMS

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	0.94
Rank	#197	Overall
	#92	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

druid.apache.org

www.faircom.com/products/faircom-db

hive.apache.org

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

www.snowflake.com

Technical documentation

druid.apache.org/docs/latest/design

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

cwiki.apache.org/confluence/display/Hive/Home

doc.nuodb.com

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation and contributors

FairCom Corporation

Apache Software Foundation

initially developed by Facebook

Dassault Systèmes

originally NuoDB, Inc.

Snowflake Computing Inc.

Initial release

2012

1979

2012

2013

2014

Current release

29.0.1, April 2024

V12, November 2020

3.1.3, April 2022

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Restricted, free version available

Open Source

Apache Version 2

commercial

limited edition free

commercial

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

ANSI C, C++

Java

C++

Server operating systems

Linux
OS X
Unix

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

All OS with a Java VM

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

hosted

Data scheme

yes

schema-less columns are supported

schema free, schema optional, schema required, partial schema,

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

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

SQL for querying

yes, ANSI SQL with proprietary extensions

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

JDBC
ODBC
Thrift

ADO.NET
JDBC
ODBC

CLI Client
JDBC
ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

C++
Java
PHP
Python

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

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

user defined functions and integration of map-reduce

Java, SQL

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

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

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

selectable replication factor

yes

Managed transparently by NuoDB

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

tunable from ACID to Eventually Consistent

ACID

tunable commit protocol

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

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

Fine grained access rights according to SQL-standard with additional protections for files

Access rights for users, groups and roles

Standard SQL roles/ privileges, Administrative Users

Users with fine-grained authorization concept, user roles and pluggable authentication

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