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DBMS > Bangdb vs. Google Cloud Bigtable vs. Google Cloud Datastore vs. jBASE

System Properties Comparison Bangdb vs. Google Cloud Bigtable vs. Google Cloud Datastore vs. jBASE

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Editorial information provided by DB-Engines
NameBangdb  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonjBASE  Xexclude from comparison
DescriptionConverged and high performance database for device data, events, time series, document and graphGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA robust multi-value DBMS comprising development tools and middleware
Primary database modelDocument store
Graph DBMS
Time Series DBMS
Key-value store
Wide column store
Document storeMultivalue DBMS
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.07
Rank#346  Overall
#47  Document stores
#35  Graph DBMS
#32  Time Series DBMS
Score2.97
Rank#92  Overall
#15  Key-value stores
#8  Wide column stores
Score4.13
Rank#71  Overall
#12  Document stores
Score1.36
Rank#157  Overall
#3  Multivalue DBMS
Websitebangdb.comcloud.google.com/­bigtablecloud.google.com/­datastorewww.rocketsoftware.com/­products/­rocket-multivalue-application-development-platform/­rocket-jbase
Technical documentationdocs.bangdb.comcloud.google.com/­bigtable/­docscloud.google.com/­datastore/­docsdocs.rocketsoftware.com/­bundle?labelkey=jbase_5.9
DeveloperSachin Sinha, BangDBGoogleGoogleRocket Software (formerly Zumasys)
Initial release2012201520081991
Current releaseBangDB 2.0, October 20215.7
License infoCommercial or Open SourceOpen Source infoBSD 3commercialcommercialcommercial
Cloud-based only infoOnly available as a cloud servicenoyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++
Server operating systemsLinuxhostedhostedAIX
Linux
Windows
Data schemeschema-freeschema-freeschema-freeschema-free
Typing infopredefined data types such as float or dateyes: string, long, double, int, geospatial, stream, eventsnoyes, details hereoptional
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.nononoyes
Secondary indexesyes infosecondary, composite, nested, reverse, geospatialnoyes
SQL infoSupport of SQLSQL like support with command line toolnoSQL-like query language (GQL)Embedded SQL for jBASE in BASIC
APIs and other access methodsProprietary protocol
RESTful HTTP API
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API
Supported programming languagesC
C#
C++
Java
Python
C#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
Basic
Jabbascript
Java
Server-side scripts infoStored proceduresnonousing Google App Engineyes
Triggersyes, Notifications (with Streaming only)noCallbacks using the Google Apps Engineyes
Partitioning methods infoMethods for storing different data on different nodesSharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithmShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor, Knob for CAP (enterprise version only)Internal replication in Colossus, and regional replication between two clusters in different zonesMulti-source replication using Paxosyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyes infousing Google Cloud Dataflowno
Consistency concepts infoMethods to ensure consistency in a distributed systemTunable consistency, set CAP knob accordinglyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate 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 integritynonoyes infovia ReferenceProperties or Ancestor pathsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic single-row operationsACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACID
Concurrency infoSupport for concurrent manipulation of datayes, optimistic concurrency controlyesyesyes
Durability infoSupport for making data persistentyes, implements WAL (Write ahead log) as wellyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes, run db with in-memory only modenonoyes
User concepts infoAccess controlyes (enterprise version only)Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights can be defined down to the item level

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More resources
BangdbGoogle Cloud BigtableGoogle Cloud DatastorejBASE
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