DBMS > Datastax Enterprise vs. Google BigQuery vs. Linter vs. Microsoft Access

System Properties Comparison Datastax Enterprise vs. Google BigQuery vs. Linter vs. Microsoft Access

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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

RDBMS for high security requirements

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Primary database model

Wide column store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

Spatial DBMS

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

Score	4.82
Rank	#65	Overall
	#4	Wide column stores

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	0.03
Rank	#368	Overall
	#156	Relational DBMS

Score	93.76
Rank	#12	Overall
	#8	Relational DBMS

Website

www.datastax.com/products/datastax-enterprise

cloud.google.com/bigquery

linter.ru

www.microsoft.com/en-us/microsoft-365/access

Technical documentation

docs.datastax.com

cloud.google.com/bigquery/docs

developer.microsoft.com/en-us/access

Developer

DataStax

Google

relex.ru

Microsoft

Initial release

2011

2010

1990

1992

Current release

6.8, April 2020

1902 (16.0.11328.20222), March 2019

License

Commercial or Open Source

commercial

Bundled with Microsoft Office

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.

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Implementation language

Java

C and C++

C++

Server operating systems

Linux
OS X

hosted

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Windows

Not a real database server, but making use of DLLs

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.

Secondary indexes

yes

SQL

Support of SQL

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

yes

but not compliant to any SQL standard

APIs and other access methods

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

RESTful HTTP/JSON API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

ADO.NET
DAO
ODBC
OLE DB

Supported programming languages

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

proprietary syntax with the possibility to convert from PL/SQL

yes

since Access 2010 using the ACE-engine

Triggers

yes

since Access 2010 using the ACE-engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

Source-replica replication

none

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

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

but no files for transaction logging

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

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

a simple user-level security was built in till version Access 2003

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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