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DBMS > Google Cloud Datastore vs. HBase vs. IBM Db2 warehouse vs. Stardog

System Properties Comparison Google Cloud Datastore vs. HBase vs. IBM Db2 warehouse vs. Stardog

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonHBase  Xexclude from comparisonIBM Db2 warehouse infoformerly named IBM dashDB  Xexclude from comparisonStardog  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformWide-column store based on Apache Hadoop and on concepts of BigTableCloud-based data warehousing serviceEnterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization
Primary database modelDocument storeWide column storeRelational DBMSGraph DBMS
RDF store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.36
Rank#72  Overall
#12  Document stores
Score27.97
Rank#26  Overall
#2  Wide column stores
Score1.37
Rank#160  Overall
#74  Relational DBMS
Score2.07
Rank#122  Overall
#11  Graph DBMS
#6  RDF stores
Websitecloud.google.com/­datastorehbase.apache.orgwww.ibm.com/­products/­db2/­warehousewww.stardog.com
Technical documentationcloud.google.com/­datastore/­docshbase.apache.org/­book.htmldocs.stardog.com
DeveloperGoogleApache Software Foundation infoApache top-level project, originally developed by PowersetIBMStardog-Union
Initial release2008200820142010
Current release2.3.4, January 20217.3.0, May 2020
License infoCommercial or Open SourcecommercialOpen Source infoApache version 2commercialcommercial info60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students
Cloud-based only infoOnly available as a cloud serviceyesnoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJava
Server operating systemshostedLinux
Unix
Windows infousing Cygwin
hostedLinux
macOS
Windows
Data schemeschema-freeschema-free, schema definition possibleyesschema-free and OWL/RDFS-schema support
Typing infopredefined data types such as float or dateyes, details hereoptions to bring your own types, AVROyesyes
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.nonono infoImport/export of XML data possibleno infoImport/export of XML data possible
Secondary indexesyesnoyesyes infosupports real-time indexing in full-text and geospatial
SQL infoSupport of SQLSQL-like query language (GQL)noyesYes, compatible with all major SQL variants through dedicated BI/SQL Server
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Java API
RESTful HTTP API
Thrift
.NET Client API
JDBC
ODBC
OLE DB
GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C#
C++
Groovy
Java
PHP
Python
Scala
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby
Server-side scripts infoStored proceduresusing Google App Engineyes infoCoprocessors in JavaPL/SQL, SQL PLuser defined functions and aggregates, HTTP Server extensions in Java
TriggersCallbacks using the Google Apps Engineyesyesyes infovia event handlers
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using PaxosMulti-source replication
Source-replica replication
yesMulti-source replication in HA-Cluster
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflowyesnono
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.Immediate Consistency or Eventual ConsistencyImmediate ConsistencyImmediate Consistency in HA-Cluster
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnoyesyes inforelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsSingle row ACID (across millions of columns)ACIDACID
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.noyesyesyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACfine grained access rights according to SQL-standardAccess rights for users and roles

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More resources
Google Cloud DatastoreHBaseIBM Db2 warehouse infoformerly named IBM dashDBStardog
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