DBMS > BigObject vs. Google Cloud Bigtable vs. Machbase Neo vs. OpenEdge vs. Postgres-XL

System Properties Comparison BigObject vs. Google Cloud Bigtable vs. Machbase Neo vs. OpenEdge vs. Postgres-XL

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

TimeSeries DBMS for AIoT and BigData

Application development environment with integrated database management system

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

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store
Wide column store

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	3.51
Rank	#86	Overall
	#46	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Website

bigobject.io

cloud.google.com/bigtable

machbase.com

www.progress.com/openedge

www.postgres-xl.org

Technical documentation

docs.bigobject.io

cloud.google.com/bigtable/docs

machbase.com/dbms

documentation.progress.com/output/ua/OpenEdge_latest

www.postgres-xl.org/documentation

Developer

BigObject, Inc.

Google

Machbase

Progress Software Corporation

Initial release

2015

2013

1984

2014

since 2012, originally named StormDB

Current release

V8.0, August 2023

OpenEdge 12.2, March 2020

10 R1, October 2018

License

Commercial or Open Source

commercial

free community edition available

commercial

free test version available

commercial

Open Source

Mozilla public license

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

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

hosted

Linux
macOS
Windows

AIX
HP-UX
Linux
Solaris
Windows

Linux
macOS

Data scheme

yes

schema-free

yes

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

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

close to SQL 92

yes

distributed, parallel query execution

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

JDBC
ODBC

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

Supported programming languages

C#
C++
Go
Java
JavaScript (Node.js)
Python

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

Progress proprietary ABL (Advanced Business Language)

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

Server-side scripts

Stored procedures

Lua

yes

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

since Version 11.4

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Internal replication in Colossus, and regional replication between two clusters in different zones

selectable replication factor

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

Atomic single-row operations

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

volatile and lookup table

User concepts

Access control

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

simple password-based access control

Users and groups

fine grained access rights according to SQL-standard

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