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DBMS > Datomic vs. Google Cloud Datastore vs. Ignite vs. Microsoft Access

System Properties Comparison Datomic vs. Google Cloud Datastore vs. Ignite vs. Microsoft Access

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Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonIgnite  Xexclude from comparisonMicrosoft Access  Xexclude from comparison
DescriptionDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformApache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.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.)
Primary database modelRelational DBMSDocument storeKey-value store
Relational DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.66
Rank#144  Overall
#66  Relational DBMS
Score4.36
Rank#72  Overall
#12  Document stores
Score3.11
Rank#96  Overall
#15  Key-value stores
#49  Relational DBMS
Score101.16
Rank#11  Overall
#8  Relational DBMS
Websitewww.datomic.comcloud.google.com/­datastoreignite.apache.orgwww.microsoft.com/­en-us/­microsoft-365/­access
Technical documentationdocs.datomic.comcloud.google.com/­datastore/­docsapacheignite.readme.io/­docsdeveloper.microsoft.com/­en-us/­access
DeveloperCognitectGoogleApache Software FoundationMicrosoft
Initial release2012200820151992
Current release1.0.7075, December 2023Apache Ignite 2.61902 (16.0.11328.20222), March 2019
License infoCommercial or Open Sourcecommercial infolimited edition freecommercialOpen Source infoApache 2.0commercial infoBundled with Microsoft Office
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, ClojureC++, Java, .NetC++
Server operating systemsAll OS with a Java VMhostedLinux
OS X
Solaris
Windows
Windows infoNot a real database server, but making use of DLLs
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyes, details hereyesyes
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.nonoyes
Secondary indexesyesyesyesyes
SQL infoSupport of SQLnoSQL-like query language (GQL)ANSI-99 for query and DML statements, subset of DDLyes infobut not compliant to any SQL standard
APIs and other access methodsRESTful HTTP APIgRPC (using protocol buffers) API
RESTful HTTP/JSON API
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
ADO.NET
DAO
ODBC
OLE DB
Supported programming languagesClojure
Java
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C#
C++
Java
PHP
Python
Ruby
Scala
C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET
Server-side scripts infoStored proceduresyes infoTransaction Functionsusing Google App Engineyes (compute grid and cache interceptors can be used instead)yes infosince Access 2010 using the ACE-engine
TriggersBy using transaction functionsCallbacks using the Google Apps Engineyes (cache interceptors and events)yes infosince Access 2010 using the ACE-engine
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesnone infoBut extensive use of caching in the application peersMulti-source replication using Paxosyes (replicated cache)none
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflowyes (compute grid and hadoop accelerator)no
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate 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
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDACID infobut no files for transaction logging
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 infobut no files for transaction logging
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 controlnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Security Hooks for custom implementationsno infoa simple user-level security was built in till version Access 2003

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More resources
DatomicGoogle Cloud DatastoreIgniteMicrosoft Access
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