DBMS > Google BigQuery vs. InterSystems Caché vs. Microsoft Azure Synapse Analytics vs. Microsoft Azure Table Storage vs. MySQL

System Properties Comparison Google BigQuery vs. InterSystems Caché vs. Microsoft Azure Synapse Analytics vs. Microsoft Azure Table Storage vs. MySQL

Editorial information provided by DB-Engines

Name

Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.

Description

Large scale data warehouse service with append-only tables

A multi-model DBMS and application server

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

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

Widely used open source RDBMS

Primary database model

Relational DBMS

Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

Wide column store

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store

Document store
Spatial DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

cloud.google.com/bigquery

www.intersystems.com/products/cache

azure.microsoft.com/services/synapse-analytics

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

www.mysql.com

Technical documentation

cloud.google.com/bigquery/docs

docs.intersystems.com

docs.microsoft.com/azure/synapse-analytics

dev.mysql.com/doc

Developer

Google

InterSystems

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2010

1997

2016

2012

1995

Current release

2018.1.4, May 2020

8.4.0, April 2024

License

Commercial or Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

C and C++

Server operating systems

hosted

AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

depending on used data model

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

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java

C#
Java
PHP

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Transact SQL

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding, horizontal partitioning

Sharding

Implicit feature of the cloud service

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

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

Multi-source replication
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

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

yes

not for MyISAM storage engine

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

optimistic locking

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles

yes

Access rights based on private key authentication or shared access signatures

Users with fine-grained authorization concept

no user groups or roles

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