DB-EnginesExtremeDB for everyone with an RTOSEnglish
Deutsch
Knowledge Base of Relational and NoSQL Database Management Systemsprovided by solid IT

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

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

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines
NameAmazon Neptune  Xexclude from comparisonEventStoreDB  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionFast, reliable graph database built for the cloudIndustrial-strength, open-source database solution built from the ground up for event sourcing.A 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
Event StoreWide column storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.29
Rank#113  Overall
#9  Graph DBMS
#5  RDF stores
Score1.19
Rank#173  Overall
#1  Event Stores
Score4.04
Rank#77  Overall
#6  Wide column stores
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websiteaws.amazon.com/­neptunewww.eventstore.comazure.microsoft.com/­en-us/­services/­storage/­tablesspark.apache.org/­sql
Technical documentationaws.amazon.com/­neptune/­developer-resourcesdevelopers.eventstore.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperAmazonEvent Store LimitedMicrosoftApache Software Foundation
Initial release2017201220122014
Current release21.2, February 20213.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen SourcecommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageScala
Server operating systemshostedLinux
Windows
hostedLinux
OS X
Windows
Data schemeschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesyesyes
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.nonono
Secondary indexesnonono
SQL infoSupport of SQLnonoSQL-like DML and DDL statements
APIs and other access methodsOpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin
RESTful HTTP APIJDBC
ODBC
Supported programming languagesC#
Go
Java
JavaScript
PHP
Python
Ruby
Scala
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Python
R
Scala
Server-side scripts infoStored proceduresnonono
Triggersnonono
Partitioning methods infoMethods for storing different data on different nodesnoneSharding 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 infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.none
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyes infoRelationships in graphsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDoptimistic lockingno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyes infowith encyption-at-restyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)Access rights based on private key authentication or shared access signaturesno

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources
Amazon NeptuneEventStoreDBMicrosoft Azure Table StorageSpark SQL
Recent citations in the news

Exploring new features of Apache TinkerPop 3.7.x in Amazon Neptune | Amazon Web Services
7 June 2024, AWS Blog

Building NHM London's Planetary Knowledge Base with Amazon Neptune and the Registry of Open Data on AWS ...
5 June 2024, AWS Blog

Unit testing Apache TinkerPop transactions: From TinkerGraph to Amazon Neptune | Amazon Web Services
3 June 2024, AWS Blog

AWS announces Amazon Neptune I/O-Optimized
22 February 2024, AWS Blog

Amazon Neptune Analytics is now available in the AWS Europe (London) Region
14 March 2024, AWS Blog

provided by Google News

Working with Azure to Use and Manage Data Lakes
7 March 2024, Simplilearn

How to Use C# Azure.Data.Tables SDK with Azure Cosmos DB
9 July 2021, hackernoon.com

How to use Azure Table storage in .Net
14 January 2019, InfoWorld

Quick Guide to Azure Storage Pricing
16 May 2023, DevOps.com

How to write data to Azure Table Store with an Azure Function
14 April 2017, Experts Exchange

provided by Google News

Use Amazon Athena with Spark SQL for your open-source transactional table formats | Amazon Web Services
24 January 2024, AWS Blog

What is Apache Spark? The big data platform that crushed Hadoop
3 April 2024, InfoWorld

Cracking the Apache Spark Interview: 80+ Top Questions and Answers for 2024
1 April 2024, Simplilearn

Performant IPv4 Range Spark Joins | by Jean-Claude Cote
24 January 2024, Towards Data Science

Simba Technologies(R) Introduces New, Powerful JDBC Driver With SQL Connector for Apache Spark(TM)
17 March 2024, Yahoo Singapore News

provided by Google News



Share this page

Featured Products

Milvus logo

Vector database designed for GenAI, fully equipped for enterprise implementation.
Try Managed Milvus for Free

Datastax Astra logo

Bring all your data to Generative AI applications with vector search enabled by the most scalable
vector database available.
Try for Free

Neo4j logo

See for yourself how a graph database can make your life easier.
Use Neo4j online for free.

Present your product here