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DBMS > Google Cloud Datastore vs. Hawkular Metrics vs. Netezza vs. OceanBase

System Properties Comparison Google Cloud Datastore vs. Hawkular Metrics vs. Netezza vs. OceanBase

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonNetezza infoAlso called PureData System for Analytics by IBM  Xexclude from comparisonOceanBase  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformHawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.Data warehouse and analytics appliance part of IBM PureSystemsA distributed, high available RDBMS compatible with Oracle and MySQL
Primary database modelDocument storeTime Series DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument store
Wide column store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.36
Rank#72  Overall
#12  Document stores
Score0.08
Rank#366  Overall
#39  Time Series DBMS
Score8.59
Rank#45  Overall
#29  Relational DBMS
Score1.57
Rank#149  Overall
#69  Relational DBMS
Websitecloud.google.com/­datastorewww.hawkular.orgwww.ibm.com/­products/­netezzaen.oceanbase.com
Technical documentationcloud.google.com/­datastore/­docswww.hawkular.org/­hawkular-metrics/­docs/­user-guideen.oceanbase.com/­docs/­oceanbase-database
DeveloperGoogleCommunity supported by Red HatIBMOceanBase infopreviously Alibaba and Ant Group
Initial release2008201420002010
Current release4.3.0, April 2024
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercialOpen Source infoCommercial license available
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++
Server operating systemshostedLinux
OS X
Windows
Linux infoincluded in applianceLinux
Data schemeschema-freeschema-freeyesyes
Typing infopredefined data types such as float or dateyes, details hereyesyesyes
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
Secondary indexesyesnoyesyes
SQL infoSupport of SQLSQL-like query language (GQL)noyesyes
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
HTTP RESTJDBC
ODBC
OLE DB
JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Proprietary native API
Table API
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Go
Java
Python
Ruby
C
C++
Fortran
Java
Lua
Perl
Python
R
Ada infoin MySQL-compatible model
C infoin Oracle- and MySQL- compatible models
C++ infoin Oracle- and MySQL- compatible models
D infoin MySQL-compatible model
Delphi infoin MySQL-compatible model
Eiffel infoin MySQL-compatible model
Erlang infoin MySQL-compatible model
Haskell infoin MySQL-compatible model
Java infoin Oracle- and MySQL- compatible models
JavaScript (Node.js) infoin MySQL-compatible model
Objective-C infoin MySQL-compatible model
OCaml infoin MySQL-compatible model
Perl infoin MySQL-compatible model
PHP infoin MySQL-compatible model
Python infoin MySQL-compatible model
Ruby infoin MySQL-compatible model
Scheme infoin MySQL-compatible model
Tcl infoin MySQL-compatible model
Server-side scripts infoStored proceduresusing Google App EnginenoyesPL/SQL in oracle-compatible mode, MySQL Stored Procedure in mysql-compatible mode
TriggersCallbacks using the Google Apps Engineyes infovia Hawkular Alertingnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infobased on CassandraShardinghorizontal partitioning (by hash, key, range, range columns, list, and list columns)
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using Paxosselectable replication factor infobased on CassandraSource-replica replicationMulti-source replication using Paxos
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflownoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate 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.Eventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnoACIDACID
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.nono
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)noUsers with fine-grained authorization conceptAccess rights for users, groups and roles according to SQL-standard

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More resources
Google Cloud DatastoreHawkular MetricsNetezza infoAlso called PureData System for Analytics by IBMOceanBase
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