DBMS > Datastax Enterprise vs. Google BigQuery vs. Ingres vs. MySQL

System Properties Comparison Datastax Enterprise vs. Google BigQuery vs. Ingres vs. MySQL

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Editorial information provided by DB-Engines

Name

Description

DataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.

Large scale data warehouse service with append-only tables

Well established RDBMS

Widely used open source RDBMS

Primary database model

Wide column store

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

Document store
Spatial DBMS

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

Score	5.80
Rank	#60	Overall
	#4	Wide column stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	4.11
Rank	#81	Overall
	#44	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

www.datastax.com/products/datastax-enterprise

cloud.google.com/bigquery

www.actian.com/databases/ingres

www.mysql.com

Technical documentation

docs.datastax.com

cloud.google.com/bigquery/docs

docs.actian.com/ingres

dev.mysql.com/doc

Developer

DataStax

Google

Actian Corporation

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2011

2010

1974

originally developed at University Berkely in early 1970s

1995

Current release

6.8, April 2020

11.2, May 2022

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

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Datastax Astra DB: Astra DB simplifies cloud-native Cassandra application development for your apps, microservices and functions. Deploy in minutes on AWS, Google Cloud, Azure, and have it managed for you by the experts, with serverless, pay-as-you-go pricing.

Aiven for MySQL: Fully managed MySQL, deployable in the cloud of your choice, with seamless integrations and lightning-fast setup.

Implementation language

Java

C and C++

Server operating systems

Linux
OS X

hosted

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

FreeBSD
Linux
OS X
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.

but tools for importing/exporting data from/to XML-files available

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements (CQL); Spark SQL

yes

with proprietary extensions

APIs and other access methods

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

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

.Net
Java
JavaScript
Objective-C
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

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

none

horizontal partitioning

Ingres Star to access multiple databases simultaneously

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware, advanced replication for edge computing

Ingres Replicator

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

Atomicity and isolation are supported for single operations

Since BigQuery is designed for querying data

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

MVCC

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 rights for users can be defined per object

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 with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

Specific characteristics

DataStax Enterprise is scale-out data infrastructure for enterprises that need to...
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Competitive advantages

Supporting the following application requirements: Zero downtime - Built on Apache...
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Typical application scenarios

Applications that must be massively and linearly scalable with 100% uptime and able...
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Key customers

Capital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
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Market metrics

Among the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
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Licensing and pricing models

Annual subscription
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