DBMS > Atos Standard Common Repository vs. Spark SQL vs. Transwarp Hippo vs. Yaacomo

System Properties Comparison Atos Standard Common Repository vs. Spark SQL vs. Transwarp Hippo vs. Yaacomo

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Editorial information provided by DB-Engines
Name	Atos Standard Common Repository  Xexclude from comparison	Spark SQL  Xexclude from comparison	Transwarp Hippo  Xexclude from comparison	Yaacomo  Xexclude from comparison
	This system has been discontinued and will be removed from the DB-Engines ranking.			Yaacomo seems to be discontinued and is removed from the DB-Engines ranking
Description	Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks	Spark SQL is a component on top of 'Spark Core' for structured data processing	Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets	OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing
Primary database model	Document store Key-value store	Relational DBMS	Vector DBMS	Relational DBMS
DB-Engines Ranking measures the popularity of database management systems Trend Chart		Score 18.04 Rank #33   Overall #20   Relational DBMS	Score 0.05 Rank #386   Overall #16   Vector DBMS
Website	atos.net/en/convergence-creators/portfolio/standard-common-repository	spark.apache.org/­sql	www.transwarp.cn/­en/­subproduct/­hippo	yaacomo.com
Technical documentation		spark.apache.org/­docs/­latest/­sql-programming-guide.html
Developer	Atos Convergence Creators	Apache Software Foundation		Q2WEB GmbH
Initial release	2016	2014	2023	2009
Current release	1703	3.5.0 ( 2.13), September 2023	1.0, May 2023
License Commercial or Open Source	commercial	Open Source Apache 2.0	commercial	commercial
Cloud-based only Only available as a cloud service	no	no	no	no
DBaaS offerings (sponsored links) Database as a Service Providers of DBaaS offerings, please contact us to be listed.
Implementation language	Java	Scala	C++
Server operating systems	Linux	Linux OS X Windows	Linux macOS	Android Linux Windows
Data scheme	Schema and schema-less with LDAP views	yes		yes
Typing predefined data types such as float or date	optional	yes	Vector, Numeric and String	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	no	no
Secondary indexes	yes	no	no	yes
SQL Support of SQL	no	SQL-like DML and DDL statements	no	yes
APIs and other access methods	LDAP	JDBC ODBC	RESTful HTTP API	JDBC ODBC
Supported programming languages	All languages with LDAP bindings	Java Python R Scala	C++ Java Python
Server-side scripts Stored procedures	no	no	no
Triggers	yes	no	no	yes
Partitioning methods Methods for storing different data on different nodes	Sharding cell division	yes, utilizing Spark Core	Sharding	horizontal partitioning
Replication methods Methods for redundantly storing data on multiple nodes	yes	none		Source-replica replication
MapReduce Offers an API for user-defined Map/Reduce methods			no	no
Consistency concepts Methods to ensure consistency in a distributed system	Immediate Consistency or Eventual Consistency depending on configuration		Immediate Consistency	Immediate Consistency
Foreign keys Referential integrity	no	no	no	yes
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data	Atomic execution of specific operations	no	no	ACID
Concurrency Support for concurrent manipulation of data	yes	yes	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	no	yes	yes
User concepts Access control	LDAP bind authentication	no	Role based access control and fine grained access rights	fine grained access rights according to SQL-standard
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More resources
	Atos Standard Common Repository	Spark SQL	Transwarp Hippo	Yaacomo
Recent citations in the news		Use Amazon Athena with Spark SQL for your open-source transactional table formats | Amazon Web Services 24 January 2024, AWS Blog What is Apache Spark? The big data platform that crushed Hadoop 3 April 2024, InfoWorld Cracking the Apache Spark Interview: 80+ Top Questions and Answers for 2024 1 April 2024, Simplilearn Performant IPv4 Range Spark Joins | by Jean-Claude Cote 24 January 2024, Towards Data Science Simba Technologies(R) Introduces New, Powerful JDBC Driver With SQL Connector for Apache Spark(TM) 17 March 2024, Yahoo Singapore News provided by Google News

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