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DBMS > BigchainDB vs. Datomic vs. Firebase Realtime Database vs. Microsoft Access vs. Splice Machine

System Properties Comparison BigchainDB vs. Datomic vs. Firebase Realtime Database vs. Microsoft Access vs. Splice Machine

Editorial information provided by DB-Engines
NameBigchainDB  Xexclude from comparisonDatomic  Xexclude from comparisonFirebase Realtime Database  Xexclude from comparisonMicrosoft Access  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionBigchainDB is scalable blockchain database offering decentralization, immutability and native assetsDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityCloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.Microsoft 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.)Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelDocument storeRelational DBMSDocument storeRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.79
Rank#212  Overall
#36  Document stores
Score1.59
Rank#150  Overall
#69  Relational DBMS
Score14.29
Rank#39  Overall
#6  Document stores
Score104.92
Rank#11  Overall
#8  Relational DBMS
Score0.54
Rank#250  Overall
#114  Relational DBMS
Websitewww.bigchaindb.comwww.datomic.comfirebase.google.com/­products/­realtime-databasewww.microsoft.com/­en-us/­microsoft-365/­accesssplicemachine.com
Technical documentationbigchaindb.readthedocs.io/­en/­latestdocs.datomic.comfirebase.google.com/­docs/­databasedeveloper.microsoft.com/­en-us/­accesssplicemachine.com/­how-it-works
DeveloperCognitectGoogle infoacquired by Google 2014MicrosoftSplice Machine
Initial release20162012201219922014
Current release1.0.6735, June 20231902 (16.0.11328.20222), March 20193.1, March 2021
License infoCommercial or Open SourceOpen Source infoAGPL v3commercial infolimited edition freecommercialcommercial infoBundled with Microsoft OfficeOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languagePythonJava, ClojureC++Java
Server operating systemsLinuxAll OS with a Java VMhostedWindows infoNot a real database server, but making use of DLLsLinux
OS X
Solaris
Windows
Data schemeschema-freeyesschema-freeyesyes
Typing infopredefined data types such as float or datenoyesyesyesyes
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.nonono
Secondary indexesyesyesyesyes
SQL infoSupport of SQLnononoyes infobut not compliant to any SQL standardyes
APIs and other access methodsCLI Client
RESTful HTTP API
RESTful HTTP APIAndroid
iOS
JavaScript API
RESTful HTTP API
ADO.NET
DAO
ODBC
OLE DB
JDBC
Native Spark Datasource
ODBC
Supported programming languagesGo
Haskell
Java
JavaScript
Python
Ruby
Clojure
Java
Java
JavaScript
Objective-C
C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresyes infoTransaction Functionslimited functionality with using 'rules'yes infosince Access 2010 using the ACE-engineyes infoJava
TriggersBy using transaction functionsCallbacks are triggered when data changesyes infosince Access 2010 using the ACE-engineyes
Partitioning methods infoMethods for storing different data on different nodesShardingnone infoBut extensive use of caching in the application peersnoneShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factornone infoBut extensive use of caching in the application peersnoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonononoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
Immediate Consistency
Foreign keys infoReferential integritynononoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDyesACID infobut no files for transaction loggingACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyes,with MongoDB ord RethinkDByes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyes 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.yes inforecommended only for testing and developmentyes
User concepts infoAccess controlyesnoyes, based on authentication and database rulesno infoa simple user-level security was built in till version Access 2003Access rights for users, groups and roles according to SQL-standard

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More resources
BigchainDBDatomicFirebase Realtime DatabaseMicrosoft AccessSplice Machine
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