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DBMS > Apache Phoenix vs. Google Cloud Datastore vs. Hypertable vs. JaguarDB vs. Microsoft Azure Table Storage

System Properties Comparison Apache Phoenix vs. Google Cloud Datastore vs. Hypertable vs. JaguarDB vs. Microsoft Azure Table Storage

Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonHypertable  Xexclude from comparisonJaguarDB  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparison
Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformAn open source BigTable implementation based on distributed file systems such as HadoopPerformant, highly scalable DBMS for AI and IoT applicationsA Wide Column Store for rapid development using massive semi-structured datasets
Primary database modelRelational DBMSDocument storeWide column storeKey-value store
Vector DBMS
Wide column store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Score0.00
Rank#383  Overall
#60  Key-value stores
#13  Vector DBMS
Score4.48
Rank#75  Overall
#6  Wide column stores
Websitephoenix.apache.orgcloud.google.com/­datastorewww.jaguardb.comazure.microsoft.com/­en-us/­services/­storage/­tables
Technical documentationphoenix.apache.orgcloud.google.com/­datastore/­docswww.jaguardb.com/­support.html
DeveloperApache Software FoundationGoogleHypertable Inc.DataJaguar, Inc.Microsoft
Initial release20142008200920152012
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20190.9.8.11, March 20163.3 July 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoGNU version 3. Commercial license availableOpen Source infoGPL V3.0commercial
Cloud-based only infoOnly available as a cloud servicenoyesnonoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++C++ infothe server part. Clients available in other languages
Server operating systemsLinux
Unix
Windows
hostedLinux
OS X
Windows infoan inofficial Windows port is available
Linuxhosted
Data schemeyes infolate-bound, schema-on-read capabilitiesschema-freeschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesyes, details herenoyesyes
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.nononono
Secondary indexesyesyesrestricted infoonly exact value or prefix value scansyesno
SQL infoSupport of SQLyesSQL-like query language (GQL)noA subset of ANSI SQL is implemented infobut no views, foreign keys, triggersno
APIs and other access methodsJDBCgRPC (using protocol buffers) API
RESTful HTTP/JSON API
C++ API
Thrift
JDBC
ODBC
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C++
Java
Perl
PHP
Python
Ruby
C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresuser defined functionsusing Google App Enginenonono
TriggersnoCallbacks using the Google Apps Enginenonono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Multi-source replication using Paxosselectable replication factor on file system levelMulti-source replicationyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationyes infousing Google Cloud Dataflowyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Immediate ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsnonooptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnonono
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)norights management via user accountsAccess rights based on private key authentication or shared access signatures

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More resources
Apache PhoenixGoogle Cloud DatastoreHypertableJaguarDBMicrosoft Azure Table Storage
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