DBMS > GeoSpock vs. Google BigQuery vs. Tibero vs. Warp 10

System Properties Comparison GeoSpock vs. Google BigQuery vs. Tibero vs. Warp 10

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Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Spatial and temporal data processing engine for extreme data scale

Large scale data warehouse service with append-only tables

A secure RDBMS, designed for easy portability from Oracle

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

Primary database model

Relational DBMS

Time Series DBMS

Secondary database models

Time Series DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.59
Rank	#148	Overall
	#68	Relational DBMS

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

geospock.com

cloud.google.com/bigquery

us.tmaxsoft.com/products/tibero

www.warp10.io

Technical documentation

cloud.google.com/bigquery/docs

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

www.warp10.io/content/02_Getting_started

Developer

GeoSpock

Google

TmaxSoft

SenX

Initial release

2010

2003

2015

Current release

2.0, September 2019

6, April 2015

License

Commercial or Open Source

commercial

Open Source

Apache License 2.0

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, Javascript

C and Assembler

Java

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Windows

Linux
OS X
Windows

Data scheme

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

temporal, categorical

yes

SQL

Support of SQL

ANSI SQL for query only (using Presto)

yes

APIs and other access methods

JDBC

RESTful HTTP/JSON API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

HTTP API
Jupyter
WebSocket

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Persistent Stored Procedure (PSM)

yes

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

horizontal partitioning

by range, hash, list or composite

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

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.

planned for next version

yes

User concepts

Access control

Access rights for users can be defined per table

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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