DBMS > Apache Druid vs. Databricks vs. Kinetica vs. Oracle vs. VoltDB

System Properties Comparison Apache Druid vs. Databricks vs. Kinetica vs. Oracle vs. VoltDB

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

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

Fully vectorized database across both GPUs and CPUs

Widely used RDBMS

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS
Time Series DBMS

Document store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

druid.apache.org

www.databricks.com

www.kinetica.com

www.oracle.com/database

www.voltdb.com

Technical documentation

druid.apache.org/docs/latest/design

docs.databricks.com

docs.kinetica.com

docs.oracle.com/en/database

docs.voltdb.com

Developer

Apache Software Foundation and contributors

Databricks

Kinetica

Oracle

VoltDB Inc.

Initial release

2012

2013

2012

1980

2010

Current release

29.0.1, April 2024

7.1, August 2021

23c, September 2023

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache license v2

commercial

restricted free version is available

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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, C++

C and C++

Java, C++

Server operating systems

Linux
OS X
Unix

hosted

Linux

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Linux
OS X

for development

Data scheme

yes

schema-less columns are supported

Flexible Schema (defined schema, partial schema, schema free)

yes

Schemaless in JSON and XML columns

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

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

with Databricks SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

yes

only a subset of SQL 99

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Python
R
Scala

C++
Java
JavaScript (Node.js)
Python

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

PL/SQL

also stored procedures in Java possible

Java

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

isolation level can be parameterized

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

In-memory capabilities

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

yes

GPU vRAM or System RAM

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

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 and roles on table level

fine grained access rights according to SQL-standard

Users and roles with access to stored procedures

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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