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DBMS > Hazelcast vs. Ignite vs. Microsoft Azure Data Explorer vs. Spark SQL vs. Titan

System Properties Comparison Hazelcast vs. Ignite vs. Microsoft Azure Data Explorer vs. Spark SQL vs. Titan

Editorial information provided by DB-Engines
NameHazelcast  Xexclude from comparisonIgnite  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSpark SQL  Xexclude from comparisonTitan  Xexclude from comparison
Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.
DescriptionA widely adopted in-memory data gridApache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.Fully managed big data interactive analytics platformSpark SQL is a component on top of 'Spark Core' for structured data processingTitan is a Graph DBMS optimized for distributed clusters.
Primary database modelKey-value storeKey-value store
Relational DBMS
Relational DBMS infocolumn orientedRelational DBMSGraph DBMS
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
Score5.97
Rank#57  Overall
#6  Key-value stores
Score3.16
Rank#96  Overall
#15  Key-value stores
#49  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitehazelcast.comignite.apache.orgazure.microsoft.com/­services/­data-explorerspark.apache.org/­sqlgithub.com/­thinkaurelius/­titan
Technical documentationhazelcast.org/­imdg/­docsapacheignite.readme.io/­docsdocs.microsoft.com/­en-us/­azure/­data-explorerspark.apache.org/­docs/­latest/­sql-programming-guide.htmlgithub.com/­thinkaurelius/­titan/­wiki
DeveloperHazelcastApache Software FoundationMicrosoftApache Software FoundationAurelius, owned by DataStax
Initial release20082015201920142012
Current release5.3.6, November 2023Apache Ignite 2.6cloud service with continuous releases3.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2; commercial licenses availableOpen Source infoApache 2.0commercialOpen Source infoApache 2.0Open Source infoApache license, version 2.0
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 languageJavaC++, Java, .NetScalaJava
Server operating systemsAll OS with a Java VMLinux
OS X
Solaris
Windows
hostedLinux
OS X
Windows
Linux
OS X
Unix
Windows
Data schemeschema-freeyesFixed schema with schema-less datatypes (dynamic)yesyes
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.yes infothe object must implement a serialization strategyyesyesno
Secondary indexesyesyesall fields are automatically indexednoyes
SQL infoSupport of SQLSQL-like query languageANSI-99 for query and DML statements, subset of DDLKusto Query Language (KQL), SQL subsetSQL-like DML and DDL statementsno
APIs and other access methodsJCache
JPA
Memcached protocol
RESTful HTTP API
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
JDBC
ODBC
Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster
Supported programming languages.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C#
C++
Java
PHP
Python
Ruby
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Java
Python
R
Scala
Clojure
Java
Python
Server-side scripts infoStored proceduresyes infoEvent Listeners, Executor Servicesyes (compute grid and cache interceptors can be used instead)Yes, possible languages: KQL, Python, Rnoyes
Triggersyes infoEventsyes (cache interceptors and events)yes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicynoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoImplicit feature of the cloud serviceyes, utilizing Spark Coreyes infovia pluggable storage backends
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoReplicated Mapyes (replicated cache)yes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.noneyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyes (compute grid and hadoop accelerator)Spark connector (open source): github.com/­Azure/­azure-kusto-sparkyes infovia Faunus, a graph analytics engine
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate ConsistencyEventual Consistency
Immediate Consistency
Eventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynonononoyes infoRelationships in graph
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataone or two-phase-commit; repeatable reads; read commitedACIDnonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes infoSupports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesnono
User concepts infoAccess controlRole-based access controlSecurity Hooks for custom implementationsAzure Active Directory AuthenticationnoUser authentification and security via Rexster Graph Server

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More resources
HazelcastIgniteMicrosoft Azure Data ExplorerSpark SQLTitan
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