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DBMS > AnzoGraph DB vs. Apache Druid vs. BigObject vs. Google Cloud Datastore

System Properties Comparison AnzoGraph DB vs. Apache Druid vs. BigObject vs. Google Cloud Datastore

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Editorial information provided by DB-Engines
NameAnzoGraph DB  Xexclude from comparisonApache Druid  Xexclude from comparisonBigObject  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparison
DescriptionScalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualizationOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataAnalytic DBMS for real-time computations and queriesAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform
Primary database modelGraph DBMS
RDF store
Relational DBMS
Time Series DBMS
Relational DBMS infoa hierachical model (tree) can be imposedDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.22
Rank#302  Overall
#24  Graph DBMS
#13  RDF stores
Score2.85
Rank#96  Overall
#50  Relational DBMS
#6  Time Series DBMS
Score0.13
Rank#329  Overall
#147  Relational DBMS
Score4.13
Rank#71  Overall
#12  Document stores
Websitecambridgesemantics.com/­anzographdruid.apache.orgbigobject.iocloud.google.com/­datastore
Technical documentationdocs.cambridgesemantics.com/­anzograph/­userdoc/­home.htmdruid.apache.org/­docs/­latest/­designdocs.bigobject.iocloud.google.com/­datastore/­docs
DeveloperCambridge SemanticsApache Software Foundation and contributorsBigObject, Inc.Google
Initial release2018201220152008
Current release2.3, January 202130.0.0, June 2024
License infoCommercial or Open Sourcecommercial infofree trial version availableOpen Source infoApache license v2commercial infofree community edition availablecommercial
Cloud-based only infoOnly available as a cloud servicenononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava
Server operating systemsLinuxLinux
OS X
Unix
Linux infodistributed as a docker-image
OS X infodistributed as a docker-image (boot2docker)
Windows infodistributed as a docker-image (boot2docker)
hosted
Data schemeSchema-free and OWL/RDFS-schema supportyes infoschema-less columns are supportedyesschema-free
Typing infopredefined data types such as float or dateyesyesyes, details here
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 indexesnoyesyesyes
SQL infoSupport of SQLSPARQL and SPARQL* as primary query language. Cypher preview.SQL for queryingSQL-like DML and DDL statementsSQL-like query language (GQL)
APIs and other access methodsApache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL
JDBC
RESTful HTTP/JSON API
fluentd
ODBC
RESTful HTTP API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Supported programming languagesC++
Java
Python
Clojure
JavaScript
PHP
Python
R
Ruby
Scala
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresuser defined functions and aggregatesnoLuausing Google App Engine
TriggersnononoCallbacks using the Google Apps Engine
Partitioning methods infoMethods for storing different data on different nodesAutomatic shardingSharding infomanual/auto, time-basednoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication in MPP-Clusteryes, via HDFS, S3 or other storage enginesnoneMulti-source replication using Paxos
MapReduce infoOffers an API for user-defined Map/Reduce methodsKerberos/HDFS data loadingnonoyes infousing Google Cloud Dataflow
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency in MPP-ClusterImmediate ConsistencynoneImmediate 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.
Foreign keys infoReferential integrityno infonot needed in graphsnoyes infoautomatically between fact table and dimension tablesyes infovia ReferenceProperties or Ancestor paths
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnonoACID infoSerializable Isolation within Transactions, Read Committed outside of Transactions
Concurrency infoSupport for concurrent manipulation of datayesyesyes infoRead/write lock on objects (tables, trees)yes
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.yesnoyesno
User concepts infoAccess controlAccess rights for users and rolesRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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More resources
AnzoGraph DBApache DruidBigObjectGoogle Cloud Datastore
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