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DBMS > BigObject vs. Dgraph vs. EsgynDB vs. Google Cloud Bigtable vs. HBase

System Properties Comparison BigObject vs. Dgraph vs. EsgynDB vs. Google Cloud Bigtable vs. HBase

Editorial information provided by DB-Engines
NameBigObject  Xexclude from comparisonDgraph  Xexclude from comparisonEsgynDB  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonHBase  Xexclude from comparison
DescriptionAnalytic DBMS for real-time computations and queriesDistributed and scalable native Graph DBMSEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Wide-column store based on Apache Hadoop and on concepts of BigTable
Primary database modelRelational DBMS infoa hierachical model (tree) can be imposedGraph DBMSRelational DBMSKey-value store
Wide column store
Wide column store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.13
Rank#342  Overall
#150  Relational DBMS
Score1.48
Rank#158  Overall
#15  Graph DBMS
Score0.23
Rank#319  Overall
#141  Relational DBMS
Score3.58
Rank#92  Overall
#14  Key-value stores
#8  Wide column stores
Score31.25
Rank#26  Overall
#2  Wide column stores
Websitebigobject.iodgraph.iowww.esgyn.cncloud.google.com/­bigtablehbase.apache.org
Technical documentationdocs.bigobject.iodgraph.io/­docscloud.google.com/­bigtable/­docshbase.apache.org/­book.html
DeveloperBigObject, Inc.Dgraph Labs, Inc.EsgynGoogleApache Software Foundation infoApache top-level project, originally developed by Powerset
Initial release20152016201520152008
Current release2.3.4, January 2021
License infoCommercial or Open Sourcecommercial infofree community edition availableOpen Source infoApache 2.0commercialcommercialOpen Source infoApache version 2
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoC++, JavaJava
Server operating systemsLinux infodistributed as a docker-image
OS X infodistributed as a docker-image (boot2docker)
Windows infodistributed as a docker-image (boot2docker)
Linux
OS X
Windows
LinuxhostedLinux
Unix
Windows infousing Cygwin
Data schemeyesschema-freeyesschema-freeschema-free, schema definition possible
Typing infopredefined data types such as float or dateyesyesyesnooptions to bring your own types, AVRO
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.nonononono
Secondary indexesyesyesyesnono
SQL infoSupport of SQLSQL-like DML and DDL statementsnoyesnono
APIs and other access methodsfluentd
ODBC
RESTful HTTP API
GraphQL query language
gRPC (using protocol buffers) API
HTTP API
ADO.NET
JDBC
ODBC
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Java API
RESTful HTTP API
Thrift
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
All languages supporting JDBC/ODBC/ADO.NetC#
C++
Go
Java
JavaScript (Node.js)
Python
C
C#
C++
Groovy
Java
PHP
Python
Scala
Server-side scripts infoStored proceduresLuanoJava Stored Proceduresnoyes infoCoprocessors in Java
Triggersnonononoyes
Partitioning methods infoMethods for storing different data on different nodesnoneyesShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnoneSynchronous replication via RaftMulti-source replication between multi datacentersInternal replication in Colossus, and regional replication between two clusters in different zonesMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyesyesyes
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate ConsistencyImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency or Eventual Consistency
Foreign keys infoReferential integrityyes infoautomatically between fact table and dimension tablesnoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDAtomic single-row operationsSingle row ACID (across millions of columns)
Concurrency infoSupport for concurrent manipulation of datayes infoRead/write lock on objects (tables, trees)yesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnonoyes
User concepts infoAccess controlnono infoPlanned for future releasesfine grained access rights according to SQL-standardAccess 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 & ABAC

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More resources
BigObjectDgraphEsgynDBGoogle Cloud BigtableHBase
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