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DBMS > Hyprcubd vs. Splice Machine

System Properties Comparison Hyprcubd vs. Splice Machine

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Editorial information provided by DB-Engines
NameHyprcubd  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionServerless Time Series DBMSOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelTime Series DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.00
Rank#343  Overall
#32  Time Series DBMS
Score1.02
Rank#170  Overall
#84  Relational DBMS
Websitewww.hyprcubd.comsplicemachine.com
Technical documentationdoc.splicemachine.com
DeveloperHyprcubd, Inc.Splice Machine
Initial release2014
Current release2.0
License infoCommercial or Open SourcecommercialOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoJava
Server operating systemshostedLinux
OS X
Solaris
Windows
Data schemeyesyes
Typing infopredefined data types such as float or dateyes infotime, int, uint, float, stringyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.no
Secondary indexesnoyes
SQL infoSupport of SQLSQL-like query languageyes
APIs and other access methodsgRPC (https)JDBC
Native Spark Datasource
ODBC
Supported programming languagesC#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresnoyes infoJava
Triggersnoyes
Partitioning methods infoMethods for storing different data on different nodesShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID
Concurrency infoSupport for concurrent manipulation of datanoyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controltoken accessAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
HyprcubdSplice Machine
Specific characteristicsTHE ONLY SCALE-OUT SQL RDBMS WITH BUILT-IN MACHINE LEARNING For data-rich, real-time...
» more
Competitive advantagesOperational Workloads: Full ACID support with high performance at scale Multi-row...
» more
Typical application scenariosNew AI/ML and IoT Applications Data Warehouse Offloads Application Modernization...
» more
Licensing and pricing modelsKubernetes Community Edition: https://hub.helm.sh/charts/splicemachine/splice-helm...
» more
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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
HyprcubdSplice Machine
Recent citations in the news

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9 November 2020, Database Trends and Applications

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4 November 2020, PRNewswire

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provided by Google News

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