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DBMS > GridGain vs. Hazelcast vs. Microsoft Azure Data Explorer vs. OrientDB vs. Sqrrl

System Properties Comparison GridGain vs. Hazelcast vs. Microsoft Azure Data Explorer vs. OrientDB vs. Sqrrl

Editorial information provided by DB-Engines
NameGridGain  Xexclude from comparisonHazelcast  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonOrientDB  Xexclude from comparisonSqrrl  Xexclude from comparison
Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.
DescriptionGridGain is an in-memory computing platform, built on Apache IgniteA widely adopted in-memory data gridFully managed big data interactive analytics platformMulti-model DBMS (Document, Graph, Key/Value)Adaptable, secure NoSQL built on Apache Accumulo
Primary database modelKey-value store
Relational DBMS
Key-value storeRelational DBMS infocolumn orientedDocument store
Graph DBMS
Key-value store
Document store
Graph DBMS
Key-value store
Wide column store
Secondary database modelsDocument 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
Score1.47
Rank#154  Overall
#26  Key-value stores
#72  Relational DBMS
Score5.97
Rank#57  Overall
#6  Key-value stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score3.19
Rank#93  Overall
#16  Document stores
#7  Graph DBMS
#14  Key-value stores
Websitewww.gridgain.comhazelcast.comazure.microsoft.com/­services/­data-explorerorientdb.orgsqrrl.com
Technical documentationwww.gridgain.com/­docs/­index.htmlhazelcast.org/­imdg/­docsdocs.microsoft.com/­en-us/­azure/­data-explorerwww.orientdb.com/­docs/­last/­index.html
DeveloperGridGain Systems, Inc.HazelcastMicrosoftOrientDB LTD; CallidusCloud; SAPAmazon infooriginally Sqrrl Data, Inc.
Initial release20072008201920102012
Current releaseGridGain 8.5.15.3.6, November 2023cloud service with continuous releases3.2.29, March 2024
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2; commercial licenses availablecommercialOpen Source infoApache version 2commercial
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, C++, .NetJavaJavaJava
Server operating systemsLinux
OS X
Solaris
Windows
All OS with a Java VMhostedAll OS with a Java JDK (>= JDK 6)Linux
Data schemeyesschema-freeFixed schema with schema-less datatypes (dynamic)schema-free infoSchema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")schema-free
Typing infopredefined data types such as float or dateyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyesyes
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.yesyes infothe object must implement a serialization strategyyesno
Secondary indexesyesyesall fields are automatically indexedyesyes
SQL infoSupport of SQLANSI-99 for query and DML statements, subset of DDLSQL-like query languageKusto Query Language (KQL), SQL subsetSQL-like query language, no joinsno
APIs and other access methodsHDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
JCache
JPA
Memcached protocol
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API
Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift
Supported programming languagesC#
C++
Java
PHP
Python
Ruby
Scala
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala
Actionscript
C infousing GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk
Server-side scripts infoStored proceduresyes (compute grid and cache interceptors can be used instead)yes infoEvent Listeners, Executor ServicesYes, possible languages: KQL, Python, RJava, Javascriptno
Triggersyes (cache interceptors and events)yes infoEventsyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyHooksno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoImplicit feature of the cloud serviceShardingSharding infomaking use of Hadoop
Replication methods infoMethods for redundantly storing data on multiple nodesyes (replicated cache)yes infoReplicated Mapyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Multi-source replicationselectable replication factor infomaking use of Hadoop
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes (compute grid and hadoop accelerator)yesSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno infocould be achieved with distributed queriesyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual Consistency
Immediate Consistency
Immediate Consistency infoDocument store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.
Foreign keys infoReferential integritynononoyes inforelationship in graphsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDone or two-phase-commit; repeatable reads; read commitednoACIDAtomic updates per row, document, or graph entity
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.yesyesno
User concepts infoAccess controlSecurity Hooks for custom implementationsRole-based access controlAzure Active Directory AuthenticationAccess rights for users and roles; record level security configurableCell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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GridGainHazelcastMicrosoft Azure Data ExplorerOrientDBSqrrl
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