DBMS > GeoSpock vs. Google Cloud Firestore vs. Hive vs. Microsoft Azure Table Storage vs. Snowflake

System Properties Comparison GeoSpock vs. Google Cloud Firestore vs. Hive vs. Microsoft Azure Table Storage vs. Snowflake

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

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

A Wide Column Store for rapid development using massive semi-structured datasets

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

Primary database model

Secondary database models

Time Series DBMS

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

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

geospock.com

firebase.google.com/products/firestore

hive.apache.org

azure.microsoft.com/en-us/services/storage/tables

www.snowflake.com

Technical documentation

firebase.google.com/docs/firestore

cwiki.apache.org/confluence/display/Hive/Home

docs.snowflake.net/manuals/index.html

Developer

GeoSpock

Google

Apache Software Foundation

initially developed by Facebook

Microsoft

Snowflake Computing Inc.

Initial release

2017

2012

2014

Current release

2.0, September 2019

3.1.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

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, Javascript

Java

Server operating systems

hosted

All OS with a Java VM

hosted

Data scheme

yes

schema-free

yes

schema-free

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

temporal, categorical

yes

SQL

Support of SQL

ANSI SQL for query only (using Presto)

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

JDBC
ODBC
Thrift

RESTful HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

C++
Java
PHP
Python

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

yes

user defined functions and integration of map-reduce

user defined functions

Triggers

yes, with Cloud Functions

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

Implicit feature of the cloud service

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

optimistic locking

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.

User concepts

Access control

Access rights for users can be defined per table

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

Access rights for users, groups and roles

Access rights based on private key authentication or shared access signatures

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

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