DBMS > Google BigQuery vs. Kinetica vs. Spark SQL vs. Stardog vs. STSdb

System Properties Comparison Google BigQuery vs. Kinetica vs. Spark SQL vs. Stardog vs. STSdb

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Fully vectorized database across both GPUs and CPUs

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

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Relational DBMS

Graph DBMS
RDF store

Key-value store

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

cloud.google.com/bigquery

www.kinetica.com

spark.apache.org/sql

www.stardog.com

github.com/STSSoft/STSdb4

Technical documentation

cloud.google.com/bigquery/docs

docs.kinetica.com

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

docs.stardog.com

Developer

Google

Kinetica

Apache Software Foundation

Stardog-Union

STS Soft SC

Initial release

2010

2012

2014

2010

2011

Current release

7.1, August 2021

3.5.0 ( 2.13), September 2023

7.3.0, May 2020

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Open Source

GPLv2, commercial license available

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

C, C++

Scala

Java

Server operating systems

hosted

Linux

Linux
OS X
Windows

Linux
macOS
Windows

Windows

Data scheme

yes

schema-free and OWL/RDFS-schema support

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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.

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

SQL-like DML and DDL statements

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

.NET Client API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

Java
Python
R
Scala

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

C#
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

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

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Multi-source replication in HA-Cluster

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

Since BigQuery is designed for querying 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

GPU vRAM or System RAM

yes

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles on table level

Access rights for users and roles

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