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DBMS > Datomic vs. Google Cloud Datastore vs. HugeGraph vs. Microsoft Azure Synapse Analytics vs. NSDb

System Properties Comparison Datomic vs. Google Cloud Datastore vs. HugeGraph vs. Microsoft Azure Synapse Analytics vs. NSDb

Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonHugeGraph  Xexclude from comparisonMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data Warehouse  Xexclude from comparisonNSDb  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 PlatformA fast-speed and highly-scalable Graph DBMSElastic, large scale data warehouse service leveraging the broad eco-system of SQL ServerScalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes
Primary database modelRelational DBMSDocument storeGraph DBMSRelational DBMSTime Series 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
Score0.17
Rank#335  Overall
#31  Graph DBMS
Score19.93
Rank#31  Overall
#19  Relational DBMS
Score0.08
Rank#369  Overall
#40  Time Series DBMS
Websitewww.datomic.comcloud.google.com/­datastoregithub.com/­hugegraph
hugegraph.apache.org
azure.microsoft.com/­services/­synapse-analyticsnsdb.io
Technical documentationdocs.datomic.comcloud.google.com/­datastore/­docshugegraph.apache.org/­docsdocs.microsoft.com/­azure/­synapse-analyticsnsdb.io/­Architecture
DeveloperCognitectGoogleBaiduMicrosoft
Initial release20122008201820162017
Current release1.0.7075, December 20230.9
License infoCommercial or Open Sourcecommercial infolimited edition freecommercialOpen Source infoApache Version 2.0commercialOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, ClojureJavaC++Java, Scala
Server operating systemsAll OS with a Java VMhostedLinux
macOS
Unix
hostedLinux
macOS
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyes, details hereyesyesyes: int, bigint, decimal, string
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.nonononono
Secondary indexesyesyesyes infoalso supports composite index and range indexyesall fields are automatically indexed
SQL infoSupport of SQLnoSQL-like query language (GQL)noyesSQL-like query language
APIs and other access methodsRESTful HTTP APIgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Java API
RESTful HTTP API
TinkerPop Gremlin
ADO.NET
JDBC
ODBC
gRPC
HTTP REST
WebSocket
Supported programming languagesClojure
Java
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Groovy
Java
Python
C#
Java
PHP
Java
Scala
Server-side scripts infoStored proceduresyes infoTransaction Functionsusing Google App Engineasynchronous Gremlin script jobsTransact SQLno
TriggersBy using transaction functionsCallbacks using the Google Apps Enginenono
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersShardingyes infodepending on used storage backend, e.g. Cassandra and HBaseSharding, horizontal partitioningSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnone infoBut extensive use of caching in the application peersMulti-source replication using Paxosyes infodepending on used storage backend, e.g. Cassandra and HBaseyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflowvia hugegraph-sparknono
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.Eventual ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsyes infoedges in graphno infodocs.microsoft.com/­en-us/­azure/­synapse-analytics/­sql-data-warehouse/­sql-data-warehouse-table-constraintsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyesyesUsing Apache Lucene
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)Users, roles and permissionsyes

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More resources
DatomicGoogle Cloud DatastoreHugeGraphMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data WarehouseNSDb
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