DBMS > Google Cloud Datastore vs. Hawkular Metrics vs. Infobright vs. Transbase

System Properties Comparison Google Cloud Datastore vs. Hawkular Metrics vs. Infobright vs. Transbase

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Editorial information provided by DB-Engines
Name	Google Cloud Datastore  Xexclude from comparison	Hawkular Metrics  Xexclude from comparison	Infobright  Xexclude from comparison	Transbase  Xexclude from comparison
Description	Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform	Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.	High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend	A resource-optimized, high-performance, universally applicable RDBMS
Primary database model	Document store	Time Series DBMS	Relational DBMS	Relational DBMS
DB-Engines Ranking measures the popularity of database management systems Trend Chart	Score 4.36 Rank #72   Overall #12   Document stores	Score 0.08 Rank #366   Overall #39   Time Series DBMS	Score 1.02 Rank #192   Overall #90   Relational DBMS	Score 0.17 Rank #334   Overall #148   Relational DBMS
Website	cloud.google.com/­datastore	www.hawkular.org	ignitetech.com/­softwarelibrary/­infobrightdb	www.transaction.de/­en/­products/­transbase.html
Technical documentation	cloud.google.com/­datastore/­docs	www.hawkular.org/­hawkular-metrics/­docs/­user-guide		www.transaction.de/­en/­products/­transbase/­features.html
Developer	Google	Community supported by Red Hat	Ignite Technologies Inc.; formerly InfoBright Inc.	Transaction Software GmbH
Initial release	2008	2014	2005	1987
Current release				Transbase 8.3, 2022
License Commercial or Open Source	commercial	Open Source Apache 2.0	commercial The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016	commercial free development license
Cloud-based only Only available as a cloud service	yes	no	no	no
DBaaS offerings (sponsored links) Database as a Service Providers of DBaaS offerings, please contact us to be listed.
Implementation language		Java	C	C and C++
Server operating systems	hosted	Linux OS X Windows	Linux Windows	FreeBSD Linux macOS Solaris Windows
Data scheme	schema-free	schema-free	yes	yes
Typing predefined data types such as float or date	yes, details here	yes	yes	yes
XML support Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.	no	no	no	no
Secondary indexes	yes	no	no Knowledge Grid Technology used instead	yes
SQL Support of SQL	SQL-like query language (GQL)	no	yes	yes
APIs and other access methods	gRPC (using protocol buffers) API RESTful HTTP/JSON API	HTTP REST	ADO.NET JDBC ODBC	ADO.NET JDBC ODBC Proprietary native API
Supported programming languages	.Net Go Java JavaScript (Node.js) PHP Python Ruby	Go Java Python Ruby	.Net C C# C++ D Eiffel Erlang Haskell Java Objective-C OCaml Perl PHP Python Ruby Scheme Tcl	C C# C++ Java JavaScript Kotlin Objective-C PHP Python
Server-side scripts Stored procedures	using Google App Engine	no	no	yes
Triggers	Callbacks using the Google Apps Engine	yes via Hawkular Alerting	no	yes
Partitioning methods Methods for storing different data on different nodes	Sharding	Sharding based on Cassandra	none
Replication methods Methods for redundantly storing data on multiple nodes	Multi-source replication using Paxos	selectable replication factor based on Cassandra	Source-replica replication	Source-replica replication
MapReduce Offers an API for user-defined Map/Reduce methods	yes using Google Cloud Dataflow	no	no	no
Consistency concepts Methods to ensure consistency in a distributed system	Immediate Consistency or Eventual Consistency depending on type of query and configuration Strong 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 based on Cassandra Immediate Consistency based on Cassandra	Immediate Consistency	Immediate Consistency
Foreign keys Referential integrity	yes via ReferenceProperties or Ancestor paths	no	no	yes
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data	ACID Serializable Isolation within Transactions, Read Committed outside of Transactions	no	ACID	yes
Concurrency Support for concurrent manipulation of data	yes	yes	yes	yes
Durability Support for making data persistent	yes	yes	yes	yes
In-memory capabilities Is there an option to define some or all structures to be held in-memory only.	no	no	yes	no
User concepts Access control	Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)	no	fine grained access rights according to SQL-standard exploiting MySQL or PostgreSQL frontend capabilities	fine grained access rights according to SQL-standard
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More resources
	Google Cloud Datastore	Hawkular Metrics	Infobright	Transbase
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