DBMS > GeoSpock vs. Google BigQuery vs. Microsoft Access vs. Postgres-XL vs. YDB

System Properties Comparison GeoSpock vs. Google BigQuery vs. Microsoft Access vs. Postgres-XL vs. YDB

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

Large scale data warehouse service with append-only tables

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API

Primary database model

Relational DBMS

Document store
Relational DBMS

Secondary database models

Time Series DBMS

Document store
Spatial DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	0.35
Rank	#278	Overall
	#43	Document stores
	#127	Relational DBMS

Website

geospock.com

cloud.google.com/bigquery

www.microsoft.com/en-us/microsoft-365/access

www.postgres-xl.org

github.com/ydb-platform/ydb
ydb.tech

Technical documentation

cloud.google.com/bigquery/docs

developer.microsoft.com/en-us/access

www.postgres-xl.org/documentation

ydb.tech/en/docs

Developer

GeoSpock

Google

Microsoft

Yandex

Initial release

2010

1992

2014

since 2012, originally named StormDB

2019

Current release

2.0, September 2019

1902 (16.0.11328.20222), March 2019

10 R1, October 2018

License

Commercial or Open Source

commercial

Bundled with Microsoft Office

Open Source

Mozilla public license

Open Source

Apache 2.0; 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, Javascript

C++

Server operating systems

hosted

Windows

Not a real database server, but making use of DLLs

Linux
macOS

Linux

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

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

XML type, but no XML query functionality

Secondary indexes

temporal, categorical

yes

SQL

Support of SQL

ANSI SQL for query only (using Presto)

yes

but not compliant to any SQL standard

yes

distributed, parallel query execution

SQL-like query language (YQL)

APIs and other access methods

JDBC

RESTful HTTP/JSON API

ADO.NET
DAO
ODBC
OLE DB

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

RESTful HTTP API (DynamoDB compatible)

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

since Access 2010 using the ACE-engine

user defined functions

Triggers

yes

since Access 2010 using the ACE-engine

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Active-passive shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

ACID

but no files for transaction logging

ACID

MVCC

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

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 privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

a simple user-level security was built in till version Access 2003

fine grained access rights according to SQL-standard

Access rights defined for Yandex Cloud users

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