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DBMS > GridGain vs. Microsoft Azure Data Explorer vs. Netezza vs. PostgreSQL vs. SAP SQL Anywhere

System Properties Comparison GridGain vs. Microsoft Azure Data Explorer vs. Netezza vs. PostgreSQL vs. SAP SQL Anywhere

Editorial information provided by DB-Engines
NameGridGain  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonNetezza infoAlso called PureData System for Analytics by IBM  Xexclude from comparisonPostgreSQL  Xexclude from comparisonSAP SQL Anywhere infoformerly called Adaptive Server Anywhere  Xexclude from comparison
DescriptionGridGain is an in-memory computing platform, built on Apache IgniteFully managed big data interactive analytics platformData warehouse and analytics appliance part of IBM PureSystemsWidely used open source RDBMS infoDeveloped as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQLRDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments
Primary database modelKey-value store
Relational DBMS
Relational DBMS infocolumn orientedRelational DBMSRelational DBMS infowith object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.Relational DBMS
Secondary database modelsDocument 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
Document store
Graph DBMS infowith Apache Age
Spatial DBMS
Vector DBMS infowith pgvector extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.55
Rank#150  Overall
#26  Key-value stores
#70  Relational DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score8.59
Rank#45  Overall
#29  Relational DBMS
Score636.25
Rank#4  Overall
#4  Relational DBMS
Score3.95
Rank#80  Overall
#42  Relational DBMS
Websitewww.gridgain.comazure.microsoft.com/­services/­data-explorerwww.ibm.com/­products/­netezzawww.postgresql.orgwww.sap.com/­products/­technology-platform/­sql-anywhere.html
Technical documentationwww.gridgain.com/­docs/­index.htmldocs.microsoft.com/­en-us/­azure/­data-explorerwww.postgresql.org/­docshelp.sap.com/­docs/­SAP_SQL_Anywhere
DeveloperGridGain Systems, Inc.MicrosoftIBMPostgreSQL Global Development Group infowww.postgresql.org/­developerSAP infoformerly Sybase
Initial release2007201920001989 info1989: Postgres, 1996: PostgreSQL1992
Current releaseGridGain 8.5.1cloud service with continuous releases16.3, May 202417, July 2015
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infoBSDcommercial
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, C++, .NetC
Server operating systemsLinux
OS X
Solaris
Windows
hostedLinux infoincluded in applianceFreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows
AIX
HP-UX
Linux
OS X
Solaris
Windows
Data schemeyesFixed schema with schema-less datatypes (dynamic)yesyesyes
Typing infopredefined data types such as float or dateyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyesyesyes
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.yesyesyes infospecific XML-type available, but no XML query functionality.yes
Secondary indexesyesall fields are automatically indexedyesyesyes
SQL infoSupport of SQLANSI-99 for query and DML statements, subset of DDLKusto Query Language (KQL), SQL subsetyesyes infostandard with numerous extensionsyes
APIs and other access methodsHDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
JDBC
ODBC
OLE DB
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
ADO.NET
HTTP API
JDBC
ODBC
Supported programming languagesC#
C++
Java
PHP
Python
Ruby
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C
C++
Fortran
Java
Lua
Perl
Python
R
.Net
C
C++
Delphi
Java infoJDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
Server-side scripts infoStored proceduresyes (compute grid and cache interceptors can be used instead)Yes, possible languages: KQL, Python, Ryesuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.yes, in C/C++, Java, .Net or Perl
Triggersyes (cache interceptors and events)yes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicynoyesyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoImplicit feature of the cloud serviceShardingpartitioning by range, list and (since PostgreSQL 11) by hashnone
Replication methods infoMethods for redundantly storing data on multiple nodesyes (replicated cache)yes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replicationSource-replica replication infoother methods possible by using 3rd party extensionsSource-replica replication infoDatabase mirroring
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes (compute grid and hadoop accelerator)Spark connector (open source): github.com/­Azure/­azure-kusto-sparkyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
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.yesnonoyes
User concepts infoAccess controlSecurity Hooks for custom implementationsAzure Active Directory AuthenticationUsers with fine-grained authorization conceptfine grained access rights according to SQL-standardfine grained access rights according to SQL-standard

More information provided by the system vendor

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GridGainMicrosoft Azure Data ExplorerNetezza infoAlso called PureData System for Analytics by IBMPostgreSQLSAP SQL Anywhere infoformerly called Adaptive Server Anywhere
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