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DBMS > Google Cloud Bigtable vs. Hive vs. Microsoft Access vs. Oracle Berkeley DB

System Properties Comparison Google Cloud Bigtable vs. Hive vs. Microsoft Access vs. Oracle Berkeley DB

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Editorial information provided by DB-Engines
NameGoogle Cloud Bigtable  Xexclude from comparisonHive  Xexclude from comparisonMicrosoft Access  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparison
DescriptionGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.data warehouse software for querying and managing large distributed datasets, built on HadoopMicrosoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries. infoThe Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)Widely used in-process key-value store
Primary database modelKey-value store
Wide column store
Relational DBMSRelational DBMSKey-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score61.17
Rank#18  Overall
#12  Relational DBMS
Score104.92
Rank#11  Overall
#8  Relational DBMS
Score2.21
Rank#117  Overall
#20  Key-value stores
#3  Native XML DBMS
Websitecloud.google.com/­bigtablehive.apache.orgwww.microsoft.com/­en-us/­microsoft-365/­accesswww.oracle.com/­database/­technologies/­related/­berkeleydb.html
Technical documentationcloud.google.com/­bigtable/­docscwiki.apache.org/­confluence/­display/­Hive/­Homedeveloper.microsoft.com/­en-us/­accessdocs.oracle.com/­cd/­E17076_05/­html/­index.html
DeveloperGoogleApache Software Foundation infoinitially developed by FacebookMicrosoftOracle infooriginally developed by Sleepycat, which was acquired by Oracle
Initial release2015201219921994
Current release3.1.3, April 20221902 (16.0.11328.20222), March 201918.1.40, May 2020
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2commercial infoBundled with Microsoft OfficeOpen Source infocommercial license available
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++C, Java, C++ (depending on the Berkeley DB edition)
Server operating systemshostedAll OS with a Java VMWindows infoNot a real database server, but making use of DLLsAIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
Data schemeschema-freeyesyesschema-free
Typing infopredefined data types such as float or datenoyesyesno
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.noyes infoonly with the Berkeley DB XML edition
Secondary indexesnoyesyesyes
SQL infoSupport of SQLnoSQL-like DML and DDL statementsyes infobut not compliant to any SQL standardyes infoSQL interfaced based on SQLite is available
APIs and other access methodsgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JDBC
ODBC
Thrift
ADO.NET
DAO
ODBC
OLE DB
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
Python
C++
Java
PHP
Python
C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET
.Net infoFigaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others infoThird-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js) info3rd party binding
Perl
Python
Tcl
Server-side scripts infoStored proceduresnoyes infouser defined functions and integration of map-reduceyes infosince Access 2010 using the ACE-engineno
Triggersnonoyes infosince Access 2010 using the ACE-engineyes infoonly for the SQL API
Partitioning methods infoMethods for storing different data on different nodesShardingShardingnonenone
Replication methods infoMethods for redundantly storing data on multiple nodesInternal replication in Colossus, and regional replication between two clusters in different zonesselectable replication factornoneSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyes infoquery execution via MapReducenono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Eventual Consistency
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-row operationsnoACID infobut no files for transaction loggingACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes infobut no files for transaction loggingyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users, groups and rolesno infoa simple user-level security was built in till version Access 2003no

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More resources
Google Cloud BigtableHiveMicrosoft AccessOracle Berkeley DB
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