DBMS > EJDB vs. Google BigQuery vs. Hazelcast vs. Linter vs. Microsoft Access

System Properties Comparison EJDB vs. Google BigQuery vs. Hazelcast vs. Linter vs. Microsoft Access

Editorial information provided by DB-Engines

Name

Description

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Large scale data warehouse service with append-only tables

A widely adopted in-memory data grid

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

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS

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

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	0.12
Rank	#350	Overall
	#152	Relational DBMS

Score	101.16
Rank	#11	Overall
	#8	Relational DBMS

Website

github.com/Softmotions/ejdb

cloud.google.com/bigquery

hazelcast.com

linter.ru

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

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

cloud.google.com/bigquery/docs

hazelcast.org/imdg/docs

developer.microsoft.com/en-us/access

Developer

Softmotions

Google

Hazelcast

relex.ru

Microsoft

Initial release

2012

2010

2008

1990

1992

Current release

5.3.6, November 2023

1902 (16.0.11328.20222), March 2019

License

Commercial or Open Source

Open Source

GPLv2

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

Bundled with Microsoft Office

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 and C++

C++

Server operating systems

server-less

hosted

All OS with a Java VM

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

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

yes

but not compliant to any SQL standard

APIs and other access methods

in-process shared library

RESTful HTTP/JSON API

JCache
JPA
Memcached protocol
RESTful HTTP API

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

ADO.NET
DAO
ODBC
OLE DB

Supported programming languages

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

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

Event Listeners, Executor Services

yes

proprietary syntax with the possibility to convert from PL/SQL

yes

since Access 2010 using the ACE-engine

Triggers

yes

Events

yes

since Access 2010 using the ACE-engine

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Replicated Map

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

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

Since BigQuery is designed for querying data

one or two-phase-commit; repeatable reads; read commited

ACID

but no files for transaction logging

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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

Google Cloud Identity & Access Management (IAM)

Role-based access control

fine grained access rights according to SQL-standard

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

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