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 Impala vs. Fauna vs. Microsoft Azure Data Explorer vs. STSdb

System Properties Comparison Amazon Neptune vs. Apache Impala vs. Fauna vs. Microsoft Azure Data Explorer vs. STSdb

Editorial information provided by DB-Engines
NameAmazon Neptune  Xexclude from comparisonApache Impala  Xexclude from comparisonFauna infopreviously named FaunaDB  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSTSdb  Xexclude from comparison
DescriptionFast, reliable graph database built for the cloudAnalytic DBMS for HadoopFauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.Fully managed big data interactive analytics platformKey-Value Store with special method for indexing infooptimized for high performance using a special indexing method
Primary database modelGraph DBMS
RDF store
Relational DBMSDocument store
Graph DBMS
Relational DBMS
Time Series DBMS
Relational DBMS infocolumn orientedKey-value store
Secondary database modelsDocument storeDocument 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
Score2.29
Rank#113  Overall
#9  Graph DBMS
#5  RDF stores
Score12.45
Rank#40  Overall
#24  Relational DBMS
Score1.55
Rank#151  Overall
#26  Document stores
#14  Graph DBMS
#71  Relational DBMS
#13  Time Series DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score0.10
Rank#357  Overall
#51  Key-value stores
Websiteaws.amazon.com/­neptuneimpala.apache.orgfauna.comazure.microsoft.com/­services/­data-explorergithub.com/­STSSoft/­STSdb4
Technical documentationaws.amazon.com/­neptune/­developer-resourcesimpala.apache.org/­impala-docs.htmldocs.fauna.comdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperAmazonApache Software Foundation infoApache top-level project, originally developed by ClouderaFauna, Inc.MicrosoftSTS Soft SC
Initial release20172013201420192011
Current release4.1.0, June 2022cloud service with continuous releases4.0.8, September 2015
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2commercialcommercialOpen Source infoGPLv2, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesnoyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++ScalaC#
Server operating systemshostedLinuxhostedhostedWindows
Data schemeschema-freeyesschema-freeFixed schema with schema-less datatypes (dynamic)yes
Typing infopredefined data types such as float or dateyesyesnoyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes infoprimitive types and user defined types (classes)
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
Secondary indexesnoyesyesall fields are automatically indexedno
SQL infoSupport of SQLnoSQL-like DML and DDL statementsnoKusto Query Language (KQL), SQL subsetno
APIs and other access methodsOpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin
JDBC
ODBC
RESTful HTTP APIMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
.NET Client API
Supported programming languagesC#
Go
Java
JavaScript
PHP
Python
Ruby
Scala
All languages supporting JDBC/ODBCC#
Go
Java
JavaScript
Python
Ruby
Scala
Swift
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C#
Java
Server-side scripts infoStored proceduresnoyes infouser defined functions and integration of map-reduceuser defined functionsYes, possible languages: KQL, Python, Rno
Triggersnononoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodesnoneShardinghorizontal partitioning infoconsistent hashingSharding infoImplicit feature of the cloud servicenone
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 factorMulti-source replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.none
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infoquery execution via MapReducenoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityyes infoRelationships in graphsnoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyes infowith encyption-at-restyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)Access rights for users, groups and roles infobased on Apache Sentry and KerberosIdentity management, authentication, and access controlAzure Active Directory Authenticationno

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 ImpalaFauna infopreviously named FaunaDBMicrosoft Azure Data ExplorerSTSdb
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

AWS Weekly Roundup: LlamaIndex support for Amazon Neptune, force AWS CloudFormation stack deletion, and more ...
27 May 2024, AWS Blog

provided by Google News

Apache Impala becomes Top-Level Project
28 November 2017, SDTimes.com

Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

Apache Doris just 'graduated': Why care about this SQL data warehouse
24 June 2022, InfoWorld

Hudi: Uber Engineering’s Incremental Processing Framework on Apache Hadoop
12 March 2017, Uber

Updates & Upserts in Hadoop Ecosystem with Apache Kudu
27 October 2017, KDnuggets

provided by Google News

Fauna Launches Distributed Document-Relational Database On Google Cloud Marketplace
21 March 2024, GlobeNewswire

Slicing the Gordian Knot: A leap to real-time systems of truth
3 February 2024, SiliconANGLE News

Fauna Adds Groundbreaking New Database Language and Seamless Developer Experience to Enterprise Proven ...
22 August 2023, Business Wire

CITES Trade Database surpasses 25 million trade transaction records
3 October 2023, Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES)

Fauna Query Language tamed to appeal to developers
22 August 2023, The Register

provided by Google News

We’re retiring Azure Time Series Insights on 7 July 2024 – transition to Azure Data Explorer | Azure updates
31 May 2024, Microsoft

Update records in a Kusto Database (public preview) | Azure updates
20 February 2024, Microsoft

Public Preview: Azure Data Explorer connector for Apache Flink | Azure updates
8 January 2024, Microsoft

Announcing General Availability to migrate Virtual Network injected Azure Data Explorer Cluster to Private Endpoints ...
5 February 2024, Microsoft

New Features for graph-match KQL Operator: Enhanced Pattern Matching and Cycle Control | Azure updates
24 January 2024, Microsoft

provided by Google News



Share this page

Featured Products

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.

Milvus logo

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

Present your product here