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DBMS > Amazon Neptune vs. Cloudflare Workers KV vs. CrateDB vs. Valentina Server

System Properties Comparison Amazon Neptune vs. Cloudflare Workers KV vs. CrateDB vs. Valentina Server

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Editorial information provided by DB-Engines
NameAmazon Neptune  Xexclude from comparisonCloudflare Workers KV  Xexclude from comparisonCrateDB  Xexclude from comparisonValentina Server  Xexclude from comparison
DescriptionFast, reliable graph database built for the cloudA global, low-latency, key-value store for applications on Cloudflare with exceptionally high read volumes and low-latency.Distributed Database based on LuceneObject-relational database and reports server
Primary database modelGraph DBMS
RDF store
Key-value storeDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMS
Secondary database modelsRelational 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
Score0.39
Rank#274  Overall
#39  Key-value stores
Score0.71
Rank#227  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score0.21
Rank#325  Overall
#144  Relational DBMS
Websiteaws.amazon.com/­neptunewww.cloudflare.com/­developer-platform/­workers-kvcratedb.comwww.valentina-db.net
Technical documentationaws.amazon.com/­neptune/­developer-resourcesdevelopers.cloudflare.com/­kv/­apicratedb.com/­docsvalentina-db.com/­docs/­dokuwiki/­v5/­doku.php
DeveloperAmazonCloudflareCrateParadigma Software
Initial release2017201820131999
Current release5.7.5
License infoCommercial or Open SourcecommercialcommercialOpen Sourcecommercial
Cloud-based only infoOnly available as a cloud serviceyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.
Implementation languageJava
Server operating systemshostedhostedAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportLinux
OS X
Windows
Data schemeschema-freeschema-freeFlexible Schema (defined schema, partial schema, schema free)yes
Typing infopredefined data types such as float or dateyesnoyesyes
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 indexesnonoyesyes
SQL infoSupport of SQLnonoyes, but no triggers and constraints, and PostgreSQL compatibilityyes
APIs and other access methodsOpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin
HTTP REST
Proprietary protocol
ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
ODBC
Supported programming languagesC#
Go
Java
JavaScript
PHP
Python
Ruby
Scala
C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala
.NET
Erlang
Go infocommunity maintained client
Java
JavaScript (Node.js) infocommunity maintained client
Perl infocommunity maintained client
PHP
Python
R
Ruby infocommunity maintained client
Scala infocommunity maintained client
.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET
Server-side scripts infoStored proceduresnonouser defined functions (Javascript)yes
Triggersnononoyes
Partitioning methods infoMethods for storing different data on different nodesnoneSharding
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.yesConfigurable replication on table/partition-level
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyEventual Consistency
Read-after-write consistency on record level
Foreign keys infoReferential integrityyes infoRelationships in graphsnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnono infounique row identifiers can be used for implementing an optimistic concurrency control strategy
Concurrency infoSupport for concurrent manipulation of datayesyesyes
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.nonoyes
User concepts infoAccess controlAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)rights management via user accountsfine grained access rights according to SQL-standard
More information provided by the system vendor
Amazon NeptuneCloudflare Workers KVCrateDBValentina Server
Specific characteristicsThe enterprise database for time series, documents, and vectors. Distributed - Native...
» more
Competitive advantagesResponse time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
» more
Typical application scenarios​ IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more
Key customersAcross all continents, CrateDB is used by companies of all sizes to meet the most...
» more
Market metricsThe CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
» more
Licensing and pricing modelsSee CrateDB pricing >
» more

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More resources
Amazon NeptuneCloudflare Workers KVCrateDBValentina Server
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