DBMS > GeoMesa vs. GeoSpock vs. Snowflake

System Properties Comparison GeoMesa vs. GeoSpock vs. Snowflake

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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

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

Spatial and temporal data processing engine for extreme data scale

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

Primary database model

Spatial DBMS

Relational DBMS

Secondary database models

Time Series DBMS

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

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

www.geomesa.org

geospock.com

www.snowflake.com

Technical documentation

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

docs.snowflake.net/manuals/index.html

Developer

CCRi and others

GeoSpock

Snowflake Computing Inc.

Initial release

2014

Current release

4.0.5, February 2024

2.0, September 2019

License

Commercial or Open Source

Open Source

Apache License 2.0

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, Javascript

Server operating systems

hosted

Data scheme

yes

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

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

yes

temporal, categorical

SQL

Support of SQL

ANSI SQL for query only (using Presto)

yes

APIs and other access methods

JDBC

CLI Client
JDBC
ODBC

Supported programming languages

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

Automatic sharding

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

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

User concepts

Access control

yes

depending on the DBMS used for storage

Access rights for users can be defined per table

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

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