DBMS > GeoMesa vs. Google BigQuery vs. gStore vs. SAP HANA

System Properties Comparison GeoMesa vs. Google BigQuery vs. gStore vs. SAP HANA

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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

A native Graph DBMS to store and maintain very large RDF datasets.

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial 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	0.03
Rank	#367	Overall
	#37	Graph DBMS
	#18	RDF stores

Score	41.32
Rank	#23	Overall
	#16	Relational DBMS

Website

www.geomesa.org

cloud.google.com/bigquery

en.gstore.cn

www.sap.com/products/hana.html

Technical documentation

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

cloud.google.com/bigquery/docs

en.gstore.cn/#/enDocs

help.sap.com/hana

Developer

CCRi and others

Google

SAP

Initial release

2014

2010

2016

2010

Current release

5.0.1, July 2024

1.2, November 2023

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Open Source

BSD

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Scala

C++

Server operating systems

hosted

Linux

Appliance or cloud-service

Data scheme

yes

schema-free and OWL/RDFS-schema support

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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

HTTP API
SPARQL 1.1

JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

depending on storage layer

Immediate Consistency

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

yes

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.

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)

Users, roles and permissions, Role-Based Access Control (RBAC) supported

yes

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