DBMS > dBASE vs. Google BigQuery vs. GridGain vs. Machbase Neo vs. Splice Machine

System Properties Comparison dBASE vs. Google BigQuery vs. GridGain vs. Machbase Neo vs. Splice Machine

Editorial information provided by DB-Engines

Name

Description

dBase was one of the first databases with a development environment on PC's. Its latest version dBase V is still sold as dBase classic, which needs a DOS Emulation. The up-to-date product is dBase plus.

Large scale data warehouse service with append-only tables

GridGain is an in-memory computing platform, built on Apache Ignite

TimeSeries DBMS for AIoT and BigData

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Relational DBMS

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	9.16
Rank	#43	Overall
	#27	Relational DBMS

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Score	0.07
Rank	#348	Overall
	#34	Time Series DBMS

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Website

www.dbase.com

cloud.google.com/bigquery

www.gridgain.com

machbase.com

splicemachine.com

Technical documentation

www.dbase.com/support/knowledgebase

cloud.google.com/bigquery/docs

www.gridgain.com/docs/index.html

machbase.com/dbms

splicemachine.com/how-it-works

Developer

Asthon Tate

Google

GridGain Systems, Inc.

Machbase

Splice Machine

Initial release

1979

2010

2007

2013

2014

Current release

dBASE 2019, 2019

GridGain 8.5.1

V8.0, August 2023

3.1, March 2021

License

Commercial or Open Source

commercial

commercial, open source

commercial

free test version available

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net, Python, REST, SQL

Java

Server operating systems

DOS

dBase Classic
Windows

dBase Pro

hosted

Linux
OS X
Solaris
Windows
z/OS

Linux
macOS
Windows

Linux
OS X
Solaris
Windows

Data scheme

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

ANSI-99 for query and DML statements, subset of DDL

SQL-like query language

yes

APIs and other access methods

none

The IDE can access other DBMS or ODBC-sources.

RESTful HTTP/JSON API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

JDBC
Native Spark Datasource
ODBC

Supported programming languages

dBase proprietary IDE

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

The IDE can access stored procedures in other database systems.

user defined functions

in JavaScript

yes (compute grid and cache interceptors can be used instead)

yes

Java

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes (replicated cache)

selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

not for dBase internal data, but IDE does support transactions when accessing external DBMS

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

volatile and lookup table

yes

User concepts

Access control

Access rights for users and roles

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

Google Cloud Identity & Access Management (IAM)

Role-based access control
Security Hooks for custom implementations

simple password-based access control

Access rights for users, groups and roles according to SQL-standard

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