DBMS > Kinetica vs. RavenDB vs. Snowflake vs. Spark SQL vs. Warp 10

System Properties Comparison Kinetica vs. RavenDB vs. Snowflake vs. Spark SQL vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

Fully vectorized database across both GPUs and CPUs

Open Source Operational and Transactional Enterprise NoSQL Document Database

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.07
Rank	#349	Overall
	#32	Time Series DBMS

Website

Technical documentation

docs.kinetica.com

ravendb.net/docs

docs.snowflake.net/manuals/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

www.warp10.io/content/02_Getting_started

Developer

Kinetica

Hibernating Rhinos

Snowflake Computing Inc.

Apache Software Foundation

SenX

Initial release

2012

2010

2014

2015

Current release

7.1, August 2021

5.4, July 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

AGPL version 3, commercial license available

commercial

Open Source

Apache 2.0

Open Source

Apache License 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

Scala

Java

Server operating systems

Linux

Linux
macOS
Raspberry Pi
Windows

hosted

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

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

yes

schema-free

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-like DML and DDL statements

SQL-like query language (RQL)

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

CLI Client
JDBC
ODBC

JDBC
ODBC

HTTP API
Jupyter
WebSocket

Supported programming languages

C++
Java
JavaScript (Node.js)
Python

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

JavaScript (Node.js)
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions

yes

WarpScript

Triggers

yes

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

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

yes, utilizing Spark Core

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication

yes

none

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID, Cluster-wide transaction available

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

GPU vRAM or System RAM

yes

User concepts

Access control

Access rights for users and roles on table level

Authorization levels configured per client per database

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

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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