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

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

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Editorial information provided by DB-Engines
NameBangdb  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonHBase  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 PlatformWide-column store based on Apache Hadoop and on concepts of BigTable
Primary database modelDocument store
Graph DBMS
Time Series DBMS
Key-value store
Wide column store
Document storeWide column store
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.08
Rank#347  Overall
#47  Document stores
#34  Graph DBMS
#31  Time Series DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score4.47
Rank#76  Overall
#12  Document stores
Score30.50
Rank#26  Overall
#2  Wide column stores
Websitebangdb.comcloud.google.com/­bigtablecloud.google.com/­datastorehbase.apache.org
Technical documentationdocs.bangdb.comcloud.google.com/­bigtable/­docscloud.google.com/­datastore/­docshbase.apache.org/­book.html
DeveloperSachin Sinha, BangDBGoogleGoogleApache Software Foundation infoApache top-level project, originally developed by Powerset
Initial release2012201520082008
Current releaseBangDB 2.0, October 20212.3.4, January 2021
License infoCommercial or Open SourceOpen Source infoBSD 3commercialcommercialOpen Source infoApache version 2
Cloud-based only infoOnly available as a cloud servicenoyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, C++Java
Server operating systemsLinuxhostedhostedLinux
Unix
Windows infousing Cygwin
Data schemeschema-freeschema-freeschema-freeschema-free, schema definition possible
Typing infopredefined data types such as float or dateyes: string, long, double, int, geospatial, stream, eventsnoyes, details hereoptions 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.nononono
Secondary indexesyes infosecondary, composite, nested, reverse, geospatialnoyesno
SQL infoSupport of SQLSQL like support with command line toolnoSQL-like query language (GQL)no
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
Java API
RESTful HTTP API
Thrift
Supported programming languagesC
C#
C++
Java
Python
C#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C#
C++
Groovy
Java
PHP
Python
Scala
Server-side scripts infoStored proceduresnonousing Google App Engineyes infoCoprocessors in Java
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 PaxosMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyes infousing Google Cloud Dataflowyes
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.Immediate Consistency or Eventual Consistency
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 TransactionsSingle row ACID (across millions of columns)
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 Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

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More resources
BangdbGoogle Cloud BigtableGoogle Cloud DatastoreHBase
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