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DBMS > GigaSpaces vs. Google Cloud Datastore vs. Netezza vs. Titan

System Properties Comparison GigaSpaces vs. Google Cloud Datastore vs. Netezza vs. Titan

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Editorial information provided by DB-Engines
NameGigaSpaces  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonNetezza infoAlso called PureData System for Analytics by IBM  Xexclude from comparisonTitan  Xexclude from comparison
Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.
DescriptionHigh performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented TransactionsAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformData warehouse and analytics appliance part of IBM PureSystemsTitan is a Graph DBMS optimized for distributed clusters.
Primary database modelDocument store
Object oriented DBMS infoValues are user defined objects
Document storeRelational DBMSGraph DBMS
Secondary database modelsGraph DBMS
Search engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.03
Rank#188  Overall
#32  Document stores
#6  Object oriented DBMS
Score4.36
Rank#72  Overall
#12  Document stores
Score8.59
Rank#45  Overall
#29  Relational DBMS
Websitewww.gigaspaces.comcloud.google.com/­datastorewww.ibm.com/­products/­netezzagithub.com/­thinkaurelius/­titan
Technical documentationdocs.gigaspaces.com/­latest/­landing.htmlcloud.google.com/­datastore/­docsgithub.com/­thinkaurelius/­titan/­wiki
DeveloperGigaspaces TechnologiesGoogleIBMAurelius, owned by DataStax
Initial release2000200820002012
Current release15.5, September 2020
License infoCommercial or Open SourceOpen Source infoApache Version 2; Commercial licenses availablecommercialcommercialOpen Source infoApache license, version 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, C++, .NetJava
Server operating systemsLinux
macOS
Solaris
Windows
hostedLinux infoincluded in applianceLinux
OS X
Unix
Windows
Data schemeschema-freeschema-freeyesyes
Typing infopredefined data types such as float or dateyesyes, details hereyesyes
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 infoXML can be used for describing objects metadatano
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSQL-99 for query and DML statementsSQL-like query language (GQL)yesno
APIs and other access methodsGigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
OLE DB
Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster
Supported programming languages.Net
C++
Java
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C++
Fortran
Java
Lua
Perl
Python
R
Clojure
Java
Python
Server-side scripts infoStored proceduresyesusing Google App Engineyesyes
Triggersyes, event driven architectureCallbacks using the Google Apps Enginenoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingyes infovia pluggable storage backends
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication infosynchronous or asynchronous
Source-replica replication infosynchronous or asynchronous
Multi-source replication using PaxosSource-replica replicationyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoMap-Reduce pattern can be built with XAP task executorsyes infousing Google Cloud Dataflowyesyes infovia Faunus, a graph analytics engine
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency infoConsistency level configurable: ALL, QUORUM, ANYImmediate 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
Immediate Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnoyes infoRelationships in graph
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoSupports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesno
User concepts infoAccess controlRole-based access controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Users with fine-grained authorization conceptUser authentification and security via Rexster Graph Server

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More resources
GigaSpacesGoogle Cloud DatastoreNetezza infoAlso called PureData System for Analytics by IBMTitan
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