DB-EnginesInfluxDB: Focus on building software with an easy-to-use serverless, scalable time series platformEnglish
Deutsch
Knowledge Base of Relational and NoSQL Database Management Systemsprovided by solid IT

DBMS > Amazon Neptune vs. Apache IoTDB vs. EsgynDB vs. Teradata Aster

System Properties Comparison Amazon Neptune vs. Apache IoTDB vs. EsgynDB vs. Teradata Aster

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines
NameAmazon Neptune  Xexclude from comparisonApache IoTDB  Xexclude from comparisonEsgynDB  Xexclude from comparisonTeradata Aster  Xexclude from comparison
Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.
DescriptionFast, reliable graph database built for the cloudAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionPlatform for big data analytics on multistructured data sources and types
Primary database modelGraph DBMS
RDF store
Time Series DBMSRelational DBMSRelational 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
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score0.25
Rank#312  Overall
#138  Relational DBMS
Websiteaws.amazon.com/­neptuneiotdb.apache.orgwww.esgyn.cn
Technical documentationaws.amazon.com/­neptune/­developer-resourcesiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.html
DeveloperAmazonApache Software FoundationEsgynTeradata
Initial release2017201820152005
Current release1.1.0, April 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0commercialcommercial
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++, Java
Server operating systemshostedAll OS with a Java VM (>= 1.8)LinuxLinux
Data schemeschema-freeyesyesFlexible Schema (defined schema, partial schema, schema free) infodefined schema within the relational store; partial schema or schema free in the Aster File Store
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.nononoyes infoin Aster File Store
Secondary indexesnoyesyesyes
SQL infoSupport of SQLnoSQL-like query languageyesyes
APIs and other access methodsOpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin
JDBC
Native API
ADO.NET
JDBC
ODBC
ADO.NET
JDBC
ODBC
OLE DB
Supported programming languagesC#
Go
Java
JavaScript
PHP
Python
Ruby
Scala
C
C#
C++
Go
Java
Python
Scala
All languages supporting JDBC/ODBC/ADO.NetC
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresnoyesJava Stored ProceduresR packages
Triggersnoyesnono
Partitioning methods infoMethods for storing different data on different nodesnonehorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingSharding
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.selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasMulti-source replication between multi datacentersyes infoDimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoIntegration with Hadoop and Sparkyesyes infoSQL Map-Reduce Framework
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityyes infoRelationships in graphsnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDACID
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)yesfine grained access rights according to SQL-standardfine grained access rights according to SQL-standard

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 NeptuneApache IoTDBEsgynDBTeradata Aster
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

AMD EPYC 4364P & 4564P @ DDR5-4800 / DDR5-5200 vs. Intel Xeon E-2488 Review
6 June 2024, Phoronix

TsFile: A Standard Format for IoT Time Series Data
27 February 2024, The New Stack

Linux 6.5 With AMD P-State EPP Default Brings Performance & Power Efficiency Benefits For Ryzen Servers
21 September 2023, Phoronix

Apache Promotes IoT Database Project
25 September 2020, Datanami

AMD EPYC 8324P / 8324PN Siena 32-Core Siena Linux Server Performance Review
10 October 2023, Phoronix

provided by Google News

Teradata Enhances Big Data Analytics Platform
31 May 2024, Data Center Knowledge

Northwestern Analytics Partners with Teradata Aster to Host Hackathon
23 May 2014, Northwestern Engineering

Teradata Provides the Simplest Way to Bring the Science of Data to the Art of Business
22 September 2011, PR Newswire

Teradata's Aster shows how the flowers of fraud bloom
23 April 2015, The Register

Teradata unveils improved QueryGrid connectors
21 April 2015, CIO

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

Neo4j logo

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

Present your product here