DBMS > Hazelcast vs. Microsoft Azure Table Storage vs. Valentina Server vs. XTDB

System Properties Comparison Hazelcast vs. Microsoft Azure Table Storage vs. Valentina Server vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

A widely adopted in-memory data grid

A Wide Column Store for rapid development using massive semi-structured datasets

Object-relational database and reports server

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Secondary database models

Document store

JSON support with IMDG 3.12

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

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

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	0.21
Rank	#325	Overall
	#144	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

hazelcast.com

azure.microsoft.com/en-us/services/storage/tables

www.valentina-db.net

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

hazelcast.org/imdg/docs

valentina-db.com/docs/dokuwiki/v5/doku.php

www.xtdb.com/docs

Developer

Hazelcast

Microsoft

Paradigma Software

Juxt Ltd.

Initial release

2008

2012

1999

2019

Current release

5.3.6, November 2023

5.7.5

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

MIT License

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

Clojure

Server operating systems

All OS with a Java VM

hosted

Linux
OS X
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

SQL-like query language

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

JCache
JPA
Memcached protocol
RESTful HTTP API

RESTful HTTP API

ODBC

HTTP REST
JDBC

Supported programming languages

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

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

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Clojure
Java

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

yes

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

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

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Role-based access control

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

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