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DBMS > EsgynDB vs. Google Cloud Bigtable vs. HugeGraph vs. SiteWhere vs. Teradata Aster

System Properties Comparison EsgynDB vs. Google Cloud Bigtable vs. HugeGraph vs. SiteWhere vs. Teradata Aster

Editorial information provided by DB-Engines
NameEsgynDB  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonHugeGraph  Xexclude from comparisonSiteWhere  Xexclude from comparisonTeradata Aster  Xexclude from comparison
Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.
DescriptionEnterprise-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.A fast-speed and highly-scalable Graph DBMSM2M integration platform for persisting/querying time series dataPlatform for big data analytics on multistructured data sources and types
Primary database modelRelational DBMSKey-value store
Wide column store
Graph DBMSTime Series DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.25
Rank#312  Overall
#138  Relational DBMS
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score0.17
Rank#335  Overall
#31  Graph DBMS
Score0.06
Rank#383  Overall
#43  Time Series DBMS
Websitewww.esgyn.cncloud.google.com/­bigtablegithub.com/­hugegraph
hugegraph.apache.org
github.com/­sitewhere/­sitewhere
Technical documentationcloud.google.com/­bigtable/­docshugegraph.apache.org/­docssitewhere1.sitewhere.io/­index.html
DeveloperEsgynGoogleBaiduSiteWhereTeradata
Initial release20152015201820102005
Current release0.9
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache Version 2.0Open Source infoCommon Public Attribution License Version 1.0commercial
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++, JavaJavaJava
Server operating systemsLinuxhostedLinux
macOS
Unix
Linux
OS X
Windows
Linux
Data schemeyesschema-freeyespredefined schemeFlexible Schema (defined schema, partial schema, schema free) infodefined schema within the relational store; partial schema or schema free in the Aster File Store
Typing infopredefined data types such as float or dateyesnoyesyesyes
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.nonononoyes infoin Aster File Store
Secondary indexesyesnoyes infoalso supports composite index and range indexnoyes
SQL infoSupport of SQLyesnononoyes
APIs and other access methodsADO.NET
JDBC
ODBC
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Java API
RESTful HTTP API
TinkerPop Gremlin
HTTP RESTADO.NET
JDBC
ODBC
OLE DB
Supported programming languagesAll languages supporting JDBC/ODBC/ADO.NetC#
C++
Go
Java
JavaScript (Node.js)
Python
Groovy
Java
Python
C
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresJava Stored Proceduresnoasynchronous Gremlin script jobsR packages
Triggersnononono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes infodepending on used storage backend, e.g. Cassandra and HBaseSharding infobased on HBaseSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication between multi datacentersInternal replication in Colossus, and regional replication between two clusters in different zonesyes infodepending on used storage backend, e.g. Cassandra and HBaseselectable replication factor infobased on HBaseyes infoDimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyesvia hugegraph-sparknoyes infoSQL Map-Reduce Framework
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Eventual ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityyesnoyes infoedges in graphnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic single-row operationsACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
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.nonoyesnono
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Users, roles and permissionsUsers with fine-grained authorization conceptfine grained access rights according to SQL-standard

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More resources
EsgynDBGoogle Cloud BigtableHugeGraphSiteWhereTeradata Aster
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