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DBMS > Amazon Neptune vs. Hazelcast vs. Microsoft Azure Table Storage vs. Spark SQL

System Properties Comparison Amazon Neptune vs. Hazelcast vs. Microsoft Azure Table Storage vs. Spark SQL

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Editorial information provided by DB-Engines
NameAmazon Neptune  Xexclude from comparisonHazelcast  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionFast, reliable graph database built for the cloudA widely adopted in-memory data gridA Wide Column Store for rapid development using massive semi-structured datasetsSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelGraph DBMS
RDF store
Key-value storeWide column storeRelational DBMS
Secondary database modelsDocument store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.29
Rank#113  Overall
#9  Graph DBMS
#5  RDF stores
Score5.46
Rank#61  Overall
#7  Key-value stores
Score4.04
Rank#77  Overall
#6  Wide column stores
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websiteaws.amazon.com/­neptunehazelcast.comazure.microsoft.com/­en-us/­services/­storage/­tablesspark.apache.org/­sql
Technical documentationaws.amazon.com/­neptune/­developer-resourceshazelcast.org/­imdg/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperAmazonHazelcastMicrosoftApache Software Foundation
Initial release2017200820122014
Current release5.3.6, November 20233.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2; commercial licenses availablecommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaScala
Server operating systemshostedAll OS with a Java VMhostedLinux
OS X
Windows
Data schemeschema-freeschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesyesyesyes
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.noyes infothe object must implement a serialization strategynono
Secondary indexesnoyesnono
SQL infoSupport of SQLnoSQL-like query languagenoSQL-like DML and DDL statements
APIs and other access methodsOpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin
JCache
JPA
Memcached protocol
RESTful HTTP API
RESTful HTTP APIJDBC
ODBC
Supported programming languagesC#
Go
Java
JavaScript
PHP
Python
Ruby
Scala
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Python
R
Scala
Server-side scripts infoStored proceduresnoyes infoEvent Listeners, Executor Servicesnono
Triggersnoyes infoEventsnono
Partitioning methods infoMethods for storing different data on different nodesnoneShardingSharding infoImplicit feature of the cloud serviceyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.yes 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 methodsnoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate Consistency
Foreign keys infoReferential integrityyes infoRelationships in graphsnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDone or two-phase-commit; repeatable reads; read commitedoptimistic lockingno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyes infowith encyption-at-restyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnono
User concepts infoAccess controlAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)Role-based access controlAccess rights based on private key authentication or shared access signaturesno

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More resources
Amazon NeptuneHazelcastMicrosoft Azure Table StorageSpark SQL
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