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DBMS > Fujitsu Enterprise Postgres vs. GridDB vs. Hazelcast vs. Microsoft Azure Data Explorer vs. Prometheus

System Properties Comparison Fujitsu Enterprise Postgres vs. GridDB vs. Hazelcast vs. Microsoft Azure Data Explorer vs. Prometheus

Editorial information provided by DB-Engines
NameFujitsu Enterprise Postgres  Xexclude from comparisonGridDB  Xexclude from comparisonHazelcast  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonPrometheus  Xexclude from comparison
DescriptionEnterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.Scalable in-memory time series database optimized for IoT and Big DataA widely adopted in-memory data gridFully managed big data interactive analytics platformOpen-source Time Series DBMS and monitoring system
Primary database modelRelational DBMSTime Series DBMSKey-value storeRelational DBMS infocolumn orientedTime Series DBMS
Secondary database modelsDocument store
Spatial DBMS
Key-value store
Relational DBMS
Document store infoJSON support with IMDG 3.12Document store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.31
Rank#285  Overall
#129  Relational DBMS
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score5.97
Rank#57  Overall
#6  Key-value stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score8.42
Rank#47  Overall
#2  Time Series DBMS
Websitewww.postgresql.fastware.comgriddb.nethazelcast.comazure.microsoft.com/­services/­data-explorerprometheus.io
Technical documentationwww.postgresql.fastware.com/­product-manualsdocs.griddb.nethazelcast.org/­imdg/­docsdocs.microsoft.com/­en-us/­azure/­data-explorerprometheus.io/­docs
DeveloperPostgreSQL Global Development Group, Fujitsu Australia Software TechnologyToshiba CorporationHazelcastMicrosoft
Initial release2013200820192015
Current releaseFujitsu Enterprise Postgres 14, January 20225.1, August 20225.3.6, November 2023cloud service with continuous releases
License infoCommercial or Open SourcecommercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoApache Version 2; commercial licenses availablecommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageCC++JavaGo
Server operating systemsLinux
Windows
LinuxAll OS with a Java VMhostedLinux
Windows
Data schemeyesyesschema-freeFixed schema with schema-less datatypes (dynamic)yes
Typing infopredefined data types such as float or dateyesyes infonumerical, string, blob, geometry, boolean, timestampyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesNumeric data only
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 infothe object must implement a serialization strategyyesno infoImport of XML data possible
Secondary indexesyesyesyesall fields are automatically indexedno
SQL infoSupport of SQLyesSQL92, SQL-like TQL (Toshiba Query Language)SQL-like query languageKusto Query Language (KQL), SQL subsetno
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
JCache
JPA
Memcached protocol
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
RESTful HTTP/JSON API
Supported programming languages.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby
Server-side scripts infoStored proceduresuser defined functionsnoyes infoEvent Listeners, Executor ServicesYes, possible languages: KQL, Python, Rno
Triggersyesyesyes infoEventsyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodespartitioning by range, list and by hashShardingShardingSharding infoImplicit feature of the cloud serviceSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationSource-replica replicationyes infoReplicated Mapyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes infoby Federation
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsyesSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate consistency within container, eventual consistency across containersImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual Consistency
Immediate Consistency
none
Foreign keys infoReferential integrityyesnononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID at container levelone or two-phase-commit; repeatable reads; read commitednono
Concurrency infoSupport for concurrent manipulation of datayes, multi-version concurrency control (MVCC)yesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesnono
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users can be defined per databaseRole-based access controlAzure Active Directory Authenticationno
More information provided by the system vendor
Fujitsu Enterprise PostgresGridDBHazelcastMicrosoft Azure Data ExplorerPrometheus
Specific characteristics100% compatible with community PostgreSQL
» more
GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantagesBuilt-in TDE and Data Masking security. In-Memory Columnar Index, and a high speed...
» more
1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosTransactional payments applications, reporting and mixed workloads.
» more
Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsOver 30 years experience in database technology. Over 20 years in Postgres development...
» more
GitHub trending repository
» more
Licensing and pricing modelsCore based licensing
» more
Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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Fujitsu Enterprise PostgresGridDBHazelcastMicrosoft Azure Data ExplorerPrometheus
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