DBMS > Google BigQuery vs. Interbase vs. Microsoft Azure Synapse Analytics vs. Percona Server for MongoDB vs. Percona Server for MySQL

System Properties Comparison Google BigQuery vs. Interbase vs. Microsoft Azure Synapse Analytics vs. Percona Server for MongoDB vs. Percona Server for MySQL

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Light-weight proven RDBMS

originally from Borland

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

Enhanced drop-in replacement for MySQL based on XtraDB or TokuDB storage engines with improved performance and additional diagnostic and management features.

Primary database model

Relational DBMS

Document store

Relational DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	4.08
Rank	#75	Overall
	#41	Relational DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	0.60
Rank	#246	Overall
	#39	Document stores

Score	2.10
Rank	#119	Overall
	#57	Relational DBMS

Website

cloud.google.com/bigquery

www.embarcadero.com/products/interbase

azure.microsoft.com/services/synapse-analytics

www.percona.com/mongodb/software/percona-server-for-mongodb

www.percona.com/software/mysql-database/percona-server

Technical documentation

cloud.google.com/bigquery/docs

docs.embarcadero.com/products/interbase

docs.microsoft.com/azure/synapse-analytics

docs.percona.com/percona-distribution-for-mongodb

www.percona.com/downloads/Percona-Server-LATEST

Developer

Google

Embarcadero

Microsoft

Percona

Initial release

2010

1984

2016

2015

2008

Current release

InterBase 2020, December 2019

3.4.10-2.10, November 2017

8.0.36-28, 2024

License

Commercial or Open Source

commercial

Open Source

GPL Version 2

Open Source

GPL version 2

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

hosted

Linux

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.

export as XML data possible

yes

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

proprietary protocol using JSON

ADO.NET
JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

C#
Java
PHP

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

Ada
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Interbase procedure and trigger language

Transact SQL

JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Interbase Change Views

yes

Source-replica replication

Multi-source replication
Source-replica replication
XtraDB Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

Transaction concepts

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

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

In-memory capabilities

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

yes

via In-Memory Engine

yes

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

yes

Access rights for users and roles

Users with fine-grained authorization concept

no user groups or roles

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