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DBMS > Apache Phoenix vs. Google Cloud Bigtable vs. Hazelcast vs. UniData,UniVerse

System Properties Comparison Apache Phoenix vs. Google Cloud Bigtable vs. Hazelcast vs. UniData,UniVerse

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonHazelcast  Xexclude from comparisonUniData,UniVerse  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.A widely adopted in-memory data gridMultiValue database and application server with SQL mapping layer and meta database capabilities
Primary database modelRelational DBMSKey-value store
Wide column store
Key-value storeMultivalue DBMS
Secondary database modelsDocument store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score5.97
Rank#57  Overall
#6  Key-value stores
Score3.16
Rank#97  Overall
#2  Multivalue DBMS
Websitephoenix.apache.orgcloud.google.com/­bigtablehazelcast.comwww.rocketsoftware.com/­products/­rocket-multivalue-application-development-platform/­rocket-unidata
Technical documentationphoenix.apache.orgcloud.google.com/­bigtable/­docshazelcast.org/­imdg/­docsdocs.rocketsoftware.com/­bundle?cluster=true&labelkey=unidata&labelkey=prod_unidata
DeveloperApache Software FoundationGoogleHazelcastRocket Software
Initial release2014201520081985
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20195.3.6, November 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache Version 2; commercial licenses availablecommercial
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaC
Server operating systemsLinux
Unix
Windows
hostedAll OS with a Java VMAIX
HP-UX
Linux
Solaris
Windows
Data schemeyes infolate-bound, schema-on-read capabilitiesschema-freeschema-freeschema-free
Typing infopredefined data types such as float or dateyesnoyesoptional
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.nonoyes infothe object must implement a serialization strategy
Secondary indexesyesnoyesyes
SQL infoSupport of SQLyesnoSQL-like query languageyes
APIs and other access methodsJDBCgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JCache
JPA
Memcached protocol
RESTful HTTP API
Java API infoJPA
JDBC
ODBC
OLE DB
Proprietary protocol
RESTful HTTP API
SOAP-based API
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
.Net
Basic infoU2 Basic
C
Java
Server-side scripts infoStored proceduresuser defined functionsnoyes infoEvent Listeners, Executor Servicesyes
Triggersnonoyes infoEventsyes infoU2 Basic
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Internal replication in Colossus, and regional replication between two clusters in different zonesyes infoReplicated MapSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationyesyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency or Eventual Consistency selectable by user infoRaft Consensus Algorithm
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic single-row operationsone or two-phase-commit; repeatable reads; read commitedACID infoconfigurable
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Role-based access controlAccess rights according to SQL-standard and operating system based

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More resources
Apache PhoenixGoogle Cloud BigtableHazelcastUniData,UniVerse
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