DBMS > Apache Druid vs. Oracle Berkeley DB vs. Snowflake vs. Valentina Server

System Properties Comparison Apache Druid vs. Oracle Berkeley DB vs. Snowflake vs. Valentina Server

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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

Widely used in-process key-value store

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

Object-relational database and reports server

Primary database model

Relational DBMS
Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

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

Score	2.85
Rank	#96	Overall
	#50	Relational DBMS
	#6	Time Series DBMS

Score	1.88
Rank	#130	Overall
	#23	Key-value stores
	#3	Native XML DBMS

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Score	0.18
Rank	#313	Overall
	#140	Relational DBMS

Website

druid.apache.org

www.oracle.com/database/technologies/related/berkeleydb.html

www.snowflake.com

www.valentina-db.net

Technical documentation

druid.apache.org/docs/latest/design

docs.oracle.com/cd/E17076_05/html/index.html

docs.snowflake.net/manuals/index.html

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Apache Software Foundation and contributors

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Snowflake Computing Inc.

Paradigma Software

Initial release

2012

1994

2014

1999

Current release

30.0.0, June 2024

18.1.40, May 2020

5.7.5

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

commercial license available

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

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

Linux
OS X
Unix

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

hosted

Linux
OS X
Windows

Data scheme

yes

schema-less columns are supported

schema-free

yes

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

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.

yes

only with the Berkeley DB XML edition

yes

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

CLI Client
JDBC
ODBC

ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

JavaScript (Node.js)
Python

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes

only for the SQL API

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Source-replica replication

yes

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

ACID

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, user roles and pluggable authentication

fine grained access rights according to SQL-standard

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