DBMS > Google Cloud Datastore vs. Interbase vs. Manticore Search vs. Microsoft Azure Table Storage vs. Transbase

System Properties Comparison Google Cloud Datastore vs. Interbase vs. Manticore Search vs. Microsoft Azure Table Storage vs. Transbase

Editorial information provided by DB-Engines

Name

Description

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Light-weight proven RDBMS

originally from Borland

Multi-storage database for search, including full-text search.

A Wide Column Store for rapid development using massive semi-structured datasets

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	4.61
Rank	#72	Overall
	#39	Relational DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

cloud.google.com/datastore

www.embarcadero.com/products/interbase

manticoresearch.com

azure.microsoft.com/en-us/services/storage/tables

www.transaction.de/en/products/transbase.html

Technical documentation

cloud.google.com/datastore/docs

docs.embarcadero.com/products/interbase

manual.manticoresearch.com

www.transaction.de/en/products/transbase/features.html

Developer

Google

Embarcadero

Manticore Software

Microsoft

Transaction Software GmbH

Initial release

2008

1984

2017

2012

1987

Current release

InterBase 2020, December 2019

6.0, February 2023

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

Open Source

GPL version 2

commercial

free development 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

C++

C and C++

Server operating systems

hosted

Android
iOS
Linux
OS X
Windows

FreeBSD
Linux
macOS
Windows

hosted

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

schema-free

yes

Fixed schema

schema-free

yes

Typing

predefined data types such as float or date

yes, details here

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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

Can index from XML

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

SQL-like query language

yes

APIs and other access methods

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

using Google App Engine

yes

Interbase procedure and trigger language

user defined functions

yes

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Interbase Change Views

Synchronous replication based on Galera library

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

yes

isolated transactions for atomic changes and binary logging for safe writes

optimistic locking

yes

Concurrency

Support for concurrent manipulation of data

yes

Multiversion concurreny control

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

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

fine grained access rights according to SQL-standard

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

More information provided by the system vendor

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