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DBMS > Hazelcast vs. Kinetica vs. MarkLogic vs. Microsoft Azure Data Explorer vs. Sqrrl

System Properties Comparison Hazelcast vs. Kinetica vs. MarkLogic vs. Microsoft Azure Data Explorer vs. Sqrrl

Editorial information provided by DB-Engines
NameHazelcast  Xexclude from comparisonKinetica  Xexclude from comparisonMarkLogic  Xexclude from comparisonMicrosoft Azure Data Explorer  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.
DescriptionA widely adopted in-memory data gridFully vectorized database across both GPUs and CPUsOperational and transactional Enterprise NoSQL databaseFully managed big data interactive analytics platformAdaptable, secure NoSQL built on Apache Accumulo
Primary database modelKey-value storeRelational DBMSDocument store
Native XML DBMS
RDF store infoas of version 7
Search engine
Relational DBMS infocolumn orientedDocument store
Graph DBMS
Key-value store
Wide column store
Secondary database modelsDocument store infoJSON support with IMDG 3.12Spatial DBMS
Time Series DBMS
Document 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
Score6.87
Rank#55  Overall
#6  Key-value stores
Score0.69
Rank#234  Overall
#107  Relational DBMS
Score6.50
Rank#56  Overall
#10  Document stores
#1  Native XML DBMS
#1  RDF stores
#5  Search engines
Score5.16
Rank#69  Overall
#37  Relational DBMS
Websitehazelcast.comwww.kinetica.comwww.marklogic.comazure.microsoft.com/­services/­data-explorersqrrl.com
Technical documentationhazelcast.org/­imdg/­docsdocs.kinetica.comdocs.marklogic.comdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperHazelcastKineticaMarkLogic Corp.MicrosoftAmazon infooriginally Sqrrl Data, Inc.
Initial release20082012200120192012
Current release5.3.6, November 20237.1, August 202111.0, December 2022cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache Version 2; commercial licenses availablecommercialcommercial inforestricted free version is availablecommercialcommercial
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 languageJavaC, C++C++Java
Server operating systemsAll OS with a Java VMLinuxLinux
OS X
Windows
hostedLinux
Data schemeschema-freeyesschema-free infoSchema can be enforcedFixed schema with schema-less datatypes (dynamic)schema-free
Typing infopredefined data types such as float or dateyesyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes
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.yes infothe object must implement a serialization strategynoyesyes
Secondary indexesyesyesyesall fields are automatically indexedyes
SQL infoSupport of SQLSQL-like query languageSQL-like DML and DDL statementsyes infoSQL92Kusto Query Language (KQL), SQL subsetno
APIs and other access methodsJCache
JPA
Memcached protocol
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
Java API
Node.js Client API
ODBC
proprietary Optic API infoProprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift
Supported programming languages.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C++
Java
JavaScript (Node.js)
Python
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Actionscript
C infousing GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk
Server-side scripts infoStored proceduresyes infoEvent Listeners, Executor Servicesuser defined functionsyes infovia XQuery or JavaScriptYes, possible languages: KQL, Python, Rno
Triggersyes infoEventsyes infotriggers when inserted values for one or more columns fall within a specified rangeyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding infoImplicit feature of the cloud serviceSharding infomaking use of Hadoop
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoReplicated MapSource-replica replicationyesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.selectable replication factor infomaking use of Hadoop
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnoyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsSpark connector (open source): github.com/­Azure/­azure-kusto-sparkyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyEventual 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 integritynoyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataone or two-phase-commit; repeatable reads; read commitednoACID infocan act as a resource manager in an XA/JTA transactionnoAtomic 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.yesyes infoGPU vRAM or System RAMyes, with Range Indexesno
User concepts infoAccess controlRole-based access controlAccess rights for users and roles on table levelRole-based access control at the document and subdocument levelsAzure Active Directory AuthenticationCell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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More resources
HazelcastKineticaMarkLogicMicrosoft Azure Data ExplorerSqrrl
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