DBMS > Atos Standard Common Repository vs. BigObject vs. Hazelcast vs. Microsoft Azure AI Search

System Properties Comparison Atos Standard Common Repository vs. BigObject vs. Hazelcast vs. Microsoft Azure AI Search

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Editorial information provided by DB-Engines
Name	Atos Standard Common Repository  Xexclude from comparison	BigObject  Xexclude from comparison	Hazelcast  Xexclude from comparison	Microsoft Azure AI Search  Xexclude from comparison
	This system has been discontinued and will be removed from the DB-Engines ranking.
Description	Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks	Analytic DBMS for real-time computations and queries	A widely adopted in-memory data grid	Search-as-a-service for web and mobile app development
Primary database model	Document store Key-value store	Relational DBMS a hierachical model (tree) can be imposed	Key-value store	Search engine
Secondary database models			Document store JSON support with IMDG 3.12	Vector DBMS
DB-Engines Ranking measures the popularity of database management systems Trend Chart		Score 0.19 Rank #329   Overall #146   Relational DBMS	Score 5.46 Rank #61   Overall #7   Key-value stores	Score 5.52 Rank #59   Overall #6   Search engines
Website	atos.net/en/convergence-creators/portfolio/standard-common-repository	bigobject.io	hazelcast.com	azure.microsoft.com/­en-us/­services/­search
Technical documentation		docs.bigobject.io	hazelcast.org/­imdg/­docs	learn.microsoft.com/­en-us/­azure/­search
Developer	Atos Convergence Creators	BigObject, Inc.	Hazelcast	Microsoft
Initial release	2016	2015	2008	2015
Current release	1703		5.3.6, November 2023	V1
License Commercial or Open Source	commercial	commercial free community edition available	Open Source Apache Version 2; commercial licenses available	commercial
Cloud-based only Only available as a cloud service	no	no	no	yes
DBaaS offerings (sponsored links) Database as a Service Providers of DBaaS offerings, please contact us to be listed.
Implementation language	Java		Java
Server operating systems	Linux	Linux distributed as a docker-image OS X distributed as a docker-image (boot2docker) Windows distributed as a docker-image (boot2docker)	All OS with a Java VM	hosted
Data scheme	Schema and schema-less with LDAP views	yes	schema-free	yes
Typing predefined data types such as float or date	optional	yes	yes	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	no	yes the object must implement a serialization strategy	no
Secondary indexes	yes	yes	yes	yes
SQL Support of SQL	no	SQL-like DML and DDL statements	SQL-like query language	no
APIs and other access methods	LDAP	fluentd ODBC RESTful HTTP API	JCache JPA Memcached protocol RESTful HTTP API	RESTful HTTP API
Supported programming languages	All languages with LDAP bindings		.Net C# C++ Clojure Go Java JavaScript (Node.js) Python Scala	C# Java JavaScript Python
Server-side scripts Stored procedures	no	Lua	yes Event Listeners, Executor Services	no
Triggers	yes	no	yes Events	no
Partitioning methods Methods for storing different data on different nodes	Sharding cell division	none	Sharding	Sharding Implicit feature of the cloud service
Replication methods Methods for redundantly storing data on multiple nodes	yes	none	yes Replicated Map	yes Implicit feature of the cloud service
MapReduce Offers an API for user-defined Map/Reduce methods		no	yes	no
Consistency concepts Methods to ensure consistency in a distributed system	Immediate Consistency or Eventual Consistency depending on configuration	none	Immediate Consistency or Eventual Consistency selectable by user Raft Consensus Algorithm	Immediate Consistency
Foreign keys Referential integrity	no	yes automatically between fact table and dimension tables	no	no
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data	Atomic execution of specific operations	no	one or two-phase-commit; repeatable reads; read commited	no
Concurrency Support for concurrent manipulation of data	yes	yes Read/write lock on objects (tables, trees)	yes	yes
Durability Support for making data persistent	yes	yes	yes	yes
In-memory capabilities Is there an option to define some or all structures to be held in-memory only.	yes	yes	yes	no
User concepts Access control	LDAP bind authentication	no	Role-based access control	yes using Azure authentication
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More resources
	Atos Standard Common Repository	BigObject	Hazelcast	Microsoft Azure AI Search
Recent citations in the news			Hazelcast Weaves Wider Logic Threads Through The Data Fabric 7 March 2024, Forbes Hazelcast 5.4 real time data processing platform boosts AI and consistency 17 April 2024, VentureBeat Hazelcast appoints Anthony Griffin as Chief Architect - 11 June 2024, Enterprise Times Hazelcast Showcases Real-Time Data Platform at 2024 Gartner Summit 15 May 2024, Datanami Real-Time Data Platform Hazelcast Introduces New Chief Technology Officer Adrian Soars 7 November 2023, Finovate provided by Google News	Announcing updates to Azure AI Search to help organizations build and scale generative AI applications 4 April 2024, Microsoft Microsoft and ServiceNow at Knowledge 2024: Introducing generative AI innovation 13 June 2024, Microsoft Azure OpenAI Service: Transforming legal practices with generative AI solutions 12 June 2024, Microsoft Public Preview of Azure OpenAI and AI Search in-app connectors for Logic Apps (Standard) | Azure updates 2 April 2024, Microsoft Raise the bar on AI-powered app development with Azure Database for PostgreSQL 5 June 2024, Microsoft provided by Google News

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