DBMS > Blueflood vs. Google BigQuery vs. Greenplum vs. InfinityDB vs. Microsoft Access

System Properties Comparison Blueflood vs. Google BigQuery vs. Greenplum vs. InfinityDB vs. Microsoft Access

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

Large scale data warehouse service with append-only tables

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

A Java embedded Key-Value Store which extends the Java Map interface

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.)

Primary database model

Secondary database models

Document store
Spatial DBMS

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

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	8.37
Rank	#48	Overall
	#30	Relational DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Website

blueflood.io

cloud.google.com/bigquery

greenplum.org

boilerbay.com

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

Technical documentation

github.com/rax-maas/blueflood/wiki

cloud.google.com/bigquery/docs

docs.greenplum.org

boilerbay.com/infinitydb/manual

developer.microsoft.com/en-us/access

Developer

Rackspace

Google

Pivotal Software Inc.

Boiler Bay Inc.

Microsoft

Initial release

2013

2010

2005

2002

1992

Current release

7.0.0, September 2023

4.0

1902 (16.0.11328.20222), March 2019

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache 2.0

commercial

Bundled with Microsoft Office

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

C++

Server operating systems

Linux
OS X

hosted

Linux

All OS with a Java VM

Windows

Not a real database server, but making use of DLLs

Data scheme

predefined scheme

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

since Version 4.2

Secondary indexes

yes

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

yes

but not compliant to any SQL standard

APIs and other access methods

HTTP REST

RESTful HTTP/JSON API

JDBC
ODBC

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

ADO.NET
DAO
ODBC
OLE DB

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
Java
Perl
Python
R

Java

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

since Access 2010 using the ACE-engine

Triggers

yes

since Access 2010 using the ACE-engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

READ-COMMITTED or SERIALIZED

Foreign keys

Referential integrity

yes

manual creation possible, using inversions based on multi-value capability

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Optimistic locking for transactions; no isolation for bulk loads

ACID

but no files for transaction logging

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

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

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