DBMS > EJDB vs. HEAVY.AI vs. Microsoft Azure Synapse Analytics vs. SiteWhere

System Properties Comparison EJDB vs. HEAVY.AI vs. Microsoft Azure Synapse Analytics vs. SiteWhere

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Editorial information provided by DB-Engines

Name

Description

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

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

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

M2M integration platform for persisting/querying time series data

Primary database model

Document store

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

github.com/Softmotions/ejdb

github.com/heavyai/heavydb
www.heavy.ai

azure.microsoft.com/services/synapse-analytics

github.com/sitewhere/sitewhere

Technical documentation

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

docs.heavy.ai

docs.microsoft.com/azure/synapse-analytics

sitewhere1.sitewhere.io/index.html

Developer

Softmotions

HEAVY.AI, Inc.

Microsoft

SiteWhere

Initial release

2012

2016

2010

Current release

5.10, January 2022

License

Commercial or Open Source

Open Source

GPLv2

Open Source

Apache Version 2; enterprise edition available

commercial

Open Source

Common Public Attribution License Version 1.0

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

C++ and CUDA

C++

Java

Server operating systems

server-less

Linux

hosted

Linux
OS X
Windows

Data scheme

schema-free

yes

predefined scheme

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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

in-process shared library

JDBC
ODBC
Thrift
Vega

ADO.NET
JDBC
ODBC

HTTP REST

Supported programming languages

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

All languages supporting JDBC/ODBC/Thrift
Python

C#
Java
PHP

Server-side scripts

Stored procedures

Transact SQL

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

Sharding, horizontal partitioning

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

yes

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

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.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes

Users with fine-grained authorization concept

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