DBMS > Atos Standard Common Repository vs. GeoSpock vs. Google BigQuery vs. Spark SQL vs. STSdb

System Properties Comparison Atos Standard Common Repository vs. GeoSpock vs. Google BigQuery vs. Spark SQL vs. STSdb

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

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

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

Spatial and temporal data processing engine for extreme data scale

Large scale data warehouse service with append-only tables

Spark SQL is a component on top of 'Spark Core' for structured data processing

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Document store
Key-value store

Relational DBMS

Key-value store

Secondary database models

Time Series DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

geospock.com

cloud.google.com/bigquery

spark.apache.org/sql

github.com/STSSoft/STSdb4

Technical documentation

cloud.google.com/bigquery/docs

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Atos Convergence Creators

GeoSpock

Google

Apache Software Foundation

STS Soft SC

Initial release

2016

2010

2014

2011

Current release

1703

2.0, September 2019

3.5.0 ( 2.13), September 2023

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

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

Java, Javascript

Scala

Server operating systems

Linux

hosted

Linux
OS X
Windows

Windows

Data scheme

Schema and schema-less with LDAP views

yes

Typing

predefined data types such as float or date

optional

yes

primitive types and user defined types (classes)

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

SQL-like DML and DDL statements

APIs and other access methods

LDAP

JDBC

RESTful HTTP/JSON API

JDBC
ODBC

.NET Client API

Supported programming languages

All languages with LDAP bindings

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java
Python
R
Scala

C#
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Automatic sharding

none

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of specific operations

Since BigQuery is designed for querying data

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.

yes

User concepts

Access control

LDAP bind authentication

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)

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