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DBMS > Amazon Aurora vs. Faircom DB vs. Google Cloud Bigtable vs. Microsoft Azure Data Explorer vs. NSDb

System Properties Comparison Amazon Aurora vs. Faircom DB vs. Google Cloud Bigtable vs. Microsoft Azure Data Explorer vs. NSDb

Editorial information provided by DB-Engines
NameAmazon Aurora  Xexclude from comparisonFaircom DB infoformerly c-treeACE  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonNSDb  Xexclude from comparison
DescriptionMySQL and PostgreSQL compatible cloud service by AmazonNative high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Fully managed big data interactive analytics platformScalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes
Primary database modelRelational DBMSKey-value store
Relational DBMS
Key-value store
Wide column store
Relational DBMS infocolumn orientedTime Series DBMS
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
Score7.91
Rank#50  Overall
#32  Relational DBMS
Score0.20
Rank#318  Overall
#48  Key-value stores
#141  Relational DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score0.00
Rank#383  Overall
#41  Time Series DBMS
Websiteaws.amazon.com/­rds/­aurorawww.faircom.com/­products/­faircom-dbcloud.google.com/­bigtableazure.microsoft.com/­services/­data-explorernsdb.io
Technical documentationdocs.aws.amazon.com/­AmazonRDS/­latest/­AuroraUserGuide/­CHAP_Aurora.htmldocs.faircom.com/­docs/­en/­UUID-7446ae34-a1a7-c843-c894-d5322e395184.htmlcloud.google.com/­bigtable/­docsdocs.microsoft.com/­en-us/­azure/­data-explorernsdb.io/­Architecture
DeveloperAmazonFairCom CorporationGoogleMicrosoft
Initial release20151979201520192017
Current releaseV12, November 2020cloud service with continuous releases
License infoCommercial or Open Sourcecommercialcommercial infoRestricted, free version availablecommercialcommercialOpen Source infoApache Version 2.0
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 languageANSI C, C++Java, Scala
Server operating systemshostedAIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows infoeasily portable to other OSs
hostedhostedLinux
macOS
Data schemeyesschema free, schema optional, schema required, partial schema,schema-freeFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesyes, ANSI SQL Types, JSON, typed binary structuresnoyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes: int, bigint, decimal, string
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.yesnonoyesno
Secondary indexesyesyesnoall fields are automatically indexedall fields are automatically indexed
SQL infoSupport of SQLyesyes, ANSI SQL with proprietary extensionsnoKusto Query Language (KQL), SQL subsetSQL-like query language
APIs and other access methodsADO.NET
JDBC
ODBC
ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
gRPC
HTTP REST
WebSocket
Supported programming languagesAda
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic
C#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Java
Scala
Server-side scripts infoStored proceduresyesyes info.Net, JavaScript, C/C++noYes, possible languages: KQL, Python, Rno
Triggersyesyesnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningFile partitioning, horizontal partitioning, sharding infoCustomizable business rules for table partitioningShardingSharding infoImplicit feature of the cloud serviceSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).Internal replication in Colossus, and regional replication between two clusters in different zonesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyesSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction
Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Eventual Consistency
Immediate Consistency
Eventual Consistency
Foreign keys infoReferential integrityyesyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDtunable from ACID to Eventually ConsistentAtomic single-row operationsnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesYes, tunable from durable to delayed durability to in-memoryyesyesUsing Apache Lucene
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesnono
User concepts infoAccess controlfine grained access rights according to SQL-standardFine grained access rights according to SQL-standard with additional protections for filesAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Azure Active Directory Authentication

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More resources
Amazon AuroraFaircom DB infoformerly c-treeACEGoogle Cloud BigtableMicrosoft Azure Data ExplorerNSDb
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