DBMS > Amazon Neptune vs. Google BigQuery vs. Interbase vs. LeanXcale vs. OpenEdge

System Properties Comparison Amazon Neptune vs. Google BigQuery vs. Interbase vs. LeanXcale vs. OpenEdge

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

Large scale data warehouse service with append-only tables

Light-weight proven RDBMS

originally from Borland

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Application development environment with integrated database management system

Primary database model

Graph DBMS
RDF store

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

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

Score	2.29
Rank	#113	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	4.08
Rank	#75	Overall
	#41	Relational DBMS

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	3.45
Rank	#85	Overall
	#46	Relational DBMS

Website

aws.amazon.com/neptune

cloud.google.com/bigquery

www.embarcadero.com/products/interbase

www.leanxcale.com

www.progress.com/openedge

Technical documentation

aws.amazon.com/neptune/developer-resources

cloud.google.com/bigquery/docs

docs.embarcadero.com/products/interbase

documentation.progress.com/output/ua/OpenEdge_latest

Developer

Amazon

Google

Embarcadero

LeanXcale

Progress Software Corporation

Initial release

2017

2010

1984

2015

1984

Current release

InterBase 2020, December 2019

OpenEdge 12.2, March 2020

License

Commercial or Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Server operating systems

hosted

Android
iOS
Linux
OS X
Windows

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

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.

export as XML data possible

yes

Secondary indexes

yes

SQL

Support of SQL

yes

through Apache Derby

yes

close to SQL 92

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

JDBC
ODBC

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby

C
Java
Scala

Progress proprietary ABL (Advanced Business Language)

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Interbase procedure and trigger language

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

since Version 11.4

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

Interbase Change Views

Source-replica 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

Relationships in graphs

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Multiversion concurreny control

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

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

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

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

Users and groups

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