DBMS > Apache Jena - TDB vs. DolphinDB vs. Google Cloud Datastore vs. Kinetica vs. Snowflake

System Properties Comparison Apache Jena - TDB vs. DolphinDB vs. Google Cloud Datastore vs. Kinetica vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

A RDF storage and query DBMS, shipped as an optional-use component of the Apache Jena framework

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Fully vectorized database across both GPUs and CPUs

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

Primary database model

Secondary database models

Relational DBMS

Spatial DBMS
Time Series DBMS

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

Score	3.75
Rank	#84	Overall
	#3	RDF stores

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

jena.apache.org/documentation/tdb/index.html

www.dolphindb.com

cloud.google.com/datastore

www.kinetica.com

www.snowflake.com

Technical documentation

jena.apache.org/documentation/tdb/index.html

docs.dolphindb.cn/en/help200/index.html

cloud.google.com/datastore/docs

docs.kinetica.com

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

originally developed by HP Labs

DolphinDB, Inc

Google

Kinetica

Snowflake Computing Inc.

Initial release

2000

2018

2008

2012

2014

Current release

4.9.0, July 2023

v2.00.4, January 2022

7.1, August 2021

License

Commercial or Open Source

Open Source

Apache License, Version 2.0

commercial

free community version available

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

C++

C, C++

Server operating systems

All OS with a Java VM

Linux
Windows

hosted

Linux

hosted

Data scheme

yes

RDF Schemas

yes

schema-free

yes

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

Typing

predefined data types such as float or date

yes

yes, details here

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 query language

SQL-like query language (GQL)

SQL-like DML and DDL statements

yes

APIs and other access methods

Fuseki

REST-style SPARQL HTTP Interface
Jena RDF API
RIO

RDF Input/Output

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

Java

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

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

C++
Java
JavaScript (Node.js)
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

using Google App Engine

user defined functions

Triggers

yes

via event handler

Callbacks using the Google Apps Engine

yes

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

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Multi-source replication using Paxos

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

TDB Transactions

yes

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

User concepts

Access control

Access control via Jena Security

Administrators, Users, Groups

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights for users and roles on table level

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

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