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DBMS > Datastax Enterprise vs. GBase vs. Hazelcast vs. Microsoft Azure Data Explorer vs. Sadas Engine

System Properties Comparison Datastax Enterprise vs. GBase vs. Hazelcast vs. Microsoft Azure Data Explorer vs. Sadas Engine

Editorial information provided by DB-Engines
NameDatastax Enterprise  Xexclude from comparisonGBase  Xexclude from comparisonHazelcast  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSadas Engine  Xexclude from comparison
DescriptionDataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.A widely adopted in-memory data gridFully managed big data interactive analytics platformSADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments
Primary database modelWide column storeRelational DBMSKey-value storeRelational DBMS infocolumn orientedRelational DBMS
Secondary database modelsDocument store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS
Document 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
Score5.80
Rank#60  Overall
#4  Wide column stores
Score1.07
Rank#185  Overall
#86  Relational DBMS
Score5.97
Rank#57  Overall
#6  Key-value stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score0.00
Rank#383  Overall
#158  Relational DBMS
Websitewww.datastax.com/­products/­datastax-enterprisewww.gbase.cnhazelcast.comazure.microsoft.com/­services/­data-explorerwww.sadasengine.com
Technical documentationdocs.datastax.comhazelcast.org/­imdg/­docsdocs.microsoft.com/­en-us/­azure/­data-explorerwww.sadasengine.com/­en/­sadas-engine-download-free-trial-and-documentation/­#documentation
DeveloperDataStaxGeneral Data Technology Co., Ltd.HazelcastMicrosoftSADAS s.r.l.
Initial release20112004200820192006
Current release6.8, April 2020GBase 8a, GBase 8s, GBase 8c5.3.6, November 2023cloud service with continuous releases8.0
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache Version 2; commercial licenses availablecommercialcommercial infofree trial version available
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.
Datastax Astra DB: Astra DB simplifies cloud-native Cassandra application development for your apps, microservices and functions. Deploy in minutes on AWS, Google Cloud, Azure, and have it managed for you by the experts, with serverless, pay-as-you-go pricing.
Implementation languageJavaC, Java, PythonJavaC++
Server operating systemsLinux
OS X
LinuxAll OS with a Java VMhostedAIX
Linux
Windows
Data schemeschema-freeyesschema-freeFixed schema with schema-less datatypes (dynamic)yes
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.noyesyes infothe object must implement a serialization strategyyesno
Secondary indexesyesyesyesall fields are automatically indexedyes
SQL infoSupport of SQLSQL-like DML and DDL statements (CQL); Spark SQLStandard with numerous extensionsSQL-like query languageKusto Query Language (KQL), SQL subsetyes
APIs and other access methodsProprietary protocol infoCQL (Cassandra Query Language)
TinkerPop Gremlin infowith DSE Graph
ADO.NET
C API
JDBC
ODBC
JCache
JPA
Memcached protocol
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
JDBC
ODBC
Proprietary protocol
Supported programming languagesC
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
C#.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net
C
C#
C++
Groovy
Java
PHP
Python
Server-side scripts infoStored proceduresnouser defined functionsyes infoEvent Listeners, Executor ServicesYes, possible languages: KQL, Python, Rno
Triggersyesyesyes infoEventsyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodesSharding infono "single point of failure"horizontal partitioning (by range, list and hash) and vertical partitioningShardingSharding infoImplicit feature of the cloud servicehorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesconfigurable replication factor, datacenter aware, advanced replication for edge computingyesyes infoReplicated Mapyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.none
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyesSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency
Tunable Consistency infoconsistency level can be individually decided with each write operation
Immediate ConsistencyImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyesnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infoAtomicity and isolation are supported for single operationsACIDone or two-phase-commit; repeatable reads; read commitedno
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.yesyesnoyes infomanaged by 'Learn by Usage'
User concepts infoAccess controlAccess rights for users can be defined per objectyesRole-based access controlAzure Active Directory AuthenticationAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
Datastax EnterpriseGBaseHazelcastMicrosoft Azure Data ExplorerSadas Engine
Specific characteristicsDataStax Enterprise is scale-out data infrastructure for enterprises that need to...
» more
Competitive advantagesSupporting the following application requirements: Zero downtime - Built on Apache...
» more
Typical application scenariosApplications that must be massively and linearly scalable with 100% uptime and able...
» more
Key customersCapital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
» more
Market metricsAmong the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
» more
Licensing and pricing modelsAnnual subscription
» more

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Datastax EnterpriseGBaseHazelcastMicrosoft Azure Data ExplorerSadas Engine
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