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DBMS > Fujitsu Enterprise Postgres vs. GigaSpaces vs. Hazelcast vs. Linter vs. Microsoft Azure Data Explorer

System Properties Comparison Fujitsu Enterprise Postgres vs. GigaSpaces vs. Hazelcast vs. Linter vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines
NameFujitsu Enterprise Postgres  Xexclude from comparisonGigaSpaces  Xexclude from comparisonHazelcast  Xexclude from comparisonLinter  Xexclude from comparisonMicrosoft Azure Data Explorer  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.High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented TransactionsA widely adopted in-memory data gridRDBMS for high security requirementsFully managed big data interactive analytics platform
Primary database modelRelational DBMSDocument store
Object oriented DBMS infoValues are user defined objects
Key-value storeRelational DBMSRelational DBMS infocolumn oriented
Secondary database modelsDocument store
Spatial DBMS
Graph DBMS
Search engine
Document store infoJSON support with IMDG 3.12Spatial DBMSDocument 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
Score0.97
Rank#192  Overall
#32  Document stores
#6  Object oriented DBMS
Score5.97
Rank#57  Overall
#6  Key-value stores
Score0.09
Rank#346  Overall
#152  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Websitewww.postgresql.fastware.comwww.gigaspaces.comhazelcast.comlinter.ruazure.microsoft.com/­services/­data-explorer
Technical documentationwww.postgresql.fastware.com/­product-manualsdocs.gigaspaces.com/­latest/­landing.htmlhazelcast.org/­imdg/­docsdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperPostgreSQL Global Development Group, Fujitsu Australia Software TechnologyGigaspaces TechnologiesHazelcastrelex.ruMicrosoft
Initial release2000200819902019
Current releaseFujitsu Enterprise Postgres 14, January 202215.5, September 20205.3.6, November 2023cloud service with continuous releases
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2; Commercial licenses availableOpen Source infoApache Version 2; commercial licenses availablecommercialcommercial
Cloud-based only infoOnly available as a cloud servicenonononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageCJava, C++, .NetJavaC and C++
Server operating systemsLinux
Windows
Linux
macOS
Solaris
Windows
All OS with a Java VMAIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows
hosted
Data schemeyesschema-freeschema-freeyesFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesyesyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types
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.no infoXML can be used for describing objects metadatayes infothe object must implement a serialization strategynoyes
Secondary indexesyesyesyesyesall fields are automatically indexed
SQL infoSupport of SQLyesSQL-99 for query and DML statementsSQL-like query languageyesKusto Query Language (KQL), SQL subset
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data
JCache
JPA
Memcached protocol
RESTful HTTP API
ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languages.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
.Net
C++
Java
Python
Scala
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresuser defined functionsyesyes infoEvent Listeners, Executor Servicesyes infoproprietary syntax with the possibility to convert from PL/SQLYes, possible languages: KQL, Python, R
Triggersyesyes, event driven architectureyes infoEventsyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodespartitioning by range, list and by hashShardingShardingnoneSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationMulti-source replication infosynchronous or asynchronous
Source-replica replication infosynchronous or asynchronous
yes infoReplicated MapSource-replica replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infoMap-Reduce pattern can be built with XAP task executorsyesnoSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency infoConsistency level configurable: ALL, QUORUM, ANYImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityyesnonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDone or two-phase-commit; repeatable reads; read commitedACIDno
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.yesyesno
User concepts infoAccess controlfine grained access rights according to SQL-standardRole-based access controlRole-based access controlfine grained access rights according to SQL-standardAzure Active Directory Authentication
More information provided by the system vendor
Fujitsu Enterprise PostgresGigaSpacesHazelcastLinterMicrosoft Azure Data Explorer
Specific characteristics100% compatible with community PostgreSQL
» more
Competitive advantagesBuilt-in TDE and Data Masking security. In-Memory Columnar Index, and a high speed...
» more
Typical application scenariosTransactional payments applications, reporting and mixed workloads.
» more
Market metricsOver 30 years experience in database technology. Over 20 years in Postgres development...
» more
Licensing and pricing modelsCore based licensing
» more

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Fujitsu Enterprise PostgresGigaSpacesHazelcastLinterMicrosoft Azure Data Explorer
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