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DBMS > Datomic vs. FatDB vs. Google Cloud Bigtable vs. Informix

System Properties Comparison Datomic vs. FatDB vs. Google Cloud Bigtable vs. Informix

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Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonFatDB  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonInformix  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
DescriptionDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityA .NET NoSQL DBMS that can integrate with and extend SQL Server.Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.A secure embeddable database from IBM, positioned besides IBM Db2 as a relatively low-cost product optimized for OLTP and Internet of Things data
Primary database modelRelational DBMSDocument store
Key-value store
Key-value store
Wide column store
Relational DBMS infoSince Version 12.10 support for JSON/BSON datatypes compatible with MongoDB
Secondary database modelsDocument store
Spatial DBMS
Time Series DBMS infowith Informix TimeSeries Extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.66
Rank#144  Overall
#66  Relational DBMS
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score17.12
Rank#34  Overall
#21  Relational DBMS
Websitewww.datomic.comcloud.google.com/­bigtablewww.ibm.com/­products/­informix
Technical documentationdocs.datomic.comcloud.google.com/­bigtable/­docsinformix.hcldoc.com
www.ibm.com/­support/­knowledgecenter/­SSGU8G/­welcomeIfxServers.html
DeveloperCognitectFatCloudGoogleIBM, HCL Technologies infoEffective May 1st, 2017, HCL took on development, technical support, and product management teams, and works jointly with IBM on product strategy, marketing, and sales.
Initial release2012201220151984
Current release1.0.7075, December 202314.10.FC5, November 2020
License infoCommercial or Open Sourcecommercial infolimited edition freecommercialcommercialcommercial infofree developer edition available
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, ClojureC#C, C++ and Java
Server operating systemsAll OS with a Java VMWindowshostedAIX
HP-UX
Linux
macOS
Solaris
Windows
Data schemeyesschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesyesnoyes infoSince Version 12.10 support for JSON/BSON datatypes
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.nono
Secondary indexesyesyesnoyes
SQL infoSupport of SQLnono infoVia inetgration in SQL Servernoyes
APIs and other access methodsRESTful HTTP API.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JDBC
JSON API infoMongoDB compatible
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API
Supported programming languagesClojure
Java
C#C#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
C
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresyes infoTransaction Functionsyes infovia applicationsnoyes
TriggersBy using transaction functionsyes infovia applicationsnoyes
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnone infoBut extensive use of caching in the application peersselectable replication factorInternal replication in Colossus, and regional replication between two clusters in different zonesMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoAtomic single-row operationsACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyesyes
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 developmentnoyes
User concepts infoAccess controlnono infoCan implement custom security layer via applicationsAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Users with fine-grained authentication, authorization, and auditing controls

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More resources
DatomicFatDBGoogle Cloud BigtableInformix
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