DBMS > GeoMesa vs. Google BigQuery vs. Interbase vs. Splice Machine vs. TimesTen

System Properties Comparison GeoMesa vs. Google BigQuery vs. Interbase vs. Splice Machine vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

Large scale data warehouse service with append-only tables

Light-weight proven RDBMS

originally from Borland

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

Primary database model

Spatial DBMS

Relational DBMS

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

Score	0.78
Rank	#214	Overall
	#4	Spatial DBMS

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	3.78
Rank	#77	Overall
	#41	Relational DBMS

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Score	1.26
Rank	#164	Overall
	#75	Relational DBMS

Website

www.geomesa.org

cloud.google.com/bigquery

www.embarcadero.com/products/interbase

splicemachine.com

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

www.geomesa.org/documentation/stable/user/index.html

cloud.google.com/bigquery/docs

docs.embarcadero.com/products/interbase

splicemachine.com/how-it-works

docs.oracle.com/en/database/other-databases/timesten/index.html

Developer

CCRi and others

Google

Embarcadero

Splice Machine

Oracle

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2014

2010

1984

2014

1998

Current release

5.0.1, July 2024

InterBase 2020, December 2019

3.1, March 2021

Release 22.1

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Open Source

AGPL 3.0, commercial license 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

Scala

Java

Server operating systems

hosted

Android
iOS
Linux
OS X
Windows

Linux
OS X
Solaris
Windows

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

Data scheme

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.

export as XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

C
C++
Java
Node.js
PL/SQL
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Interbase procedure and trigger language

yes

Java

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

Interbase Change Views

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

depending on storage layer

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

Multiversion concurreny control

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

In-memory capabilities

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

depending on storage layer

yes

User concepts

Access control

yes

depending on the DBMS used for storage

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

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

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