DBMS > 4D vs. EJDB vs. Hive vs. Microsoft Azure Synapse Analytics

System Properties Comparison 4D vs. EJDB vs. Hive vs. Microsoft Azure Synapse Analytics

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Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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

Primary database model

Relational DBMS

Document store

Relational DBMS

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

Score	2.47
Rank	#110	Overall
	#54	Relational DBMS

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Website

www.4d.com

github.com/Softmotions/ejdb

hive.apache.org

azure.microsoft.com/services/synapse-analytics

Technical documentation

developer.4d.com

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

cwiki.apache.org/confluence/display/Hive/Home

docs.microsoft.com/azure/synapse-analytics

Developer

4D, Inc

Softmotions

Apache Software Foundation

initially developed by Facebook

Microsoft

Initial release

1984

2012

2016

Current release

v20, April 2023

3.1.3, April 2022

License

Commercial or Open Source

commercial

Open Source

GPLv2

Open Source

Apache Version 2

commercial

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.

Implementation language

Java

C++

Server operating systems

OS X
Windows

server-less

All OS with a Java VM

hosted

Data scheme

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

Secondary indexes

yes

SQL

Support of SQL

yes

close to SQL 92

SQL-like DML and DDL statements

yes

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

in-process shared library

JDBC
ODBC
Thrift

ADO.NET
JDBC
ODBC

Supported programming languages

4D proprietary IDE
PHP

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

C++
Java
PHP
Python

C#
Java
PHP

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Transact SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

typically not needed, however similar functionality with collection joins possible

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

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

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.

User concepts

Access control

Users and groups

Access rights for users, groups and roles

yes

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