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DBMS > Amazon Aurora vs. Apache Impala vs. Fauna vs. Microsoft Azure Data Explorer vs. YugabyteDB

System Properties Comparison Amazon Aurora vs. Apache Impala vs. Fauna vs. Microsoft Azure Data Explorer vs. YugabyteDB

Editorial information provided by DB-Engines
NameAmazon Aurora  Xexclude from comparisonApache Impala  Xexclude from comparisonFauna infopreviously named FaunaDB  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonYugabyteDB  Xexclude from comparison
DescriptionMySQL and PostgreSQL compatible cloud service by AmazonAnalytic 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 platformHigh-performance distributed SQL database for global, internet-scale applications. Wire and feature compatible with PostgreSQL.
Primary database modelRelational DBMSRelational DBMSDocument store
Graph DBMS
Relational DBMS
Time Series DBMS
Relational DBMS infocolumn orientedRelational DBMS
Secondary database modelsDocument storeDocument 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
Document store
Wide column store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score7.57
Rank#51  Overall
#32  Relational DBMS
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
Score2.63
Rank#109  Overall
#53  Relational DBMS
Websiteaws.amazon.com/­rds/­auroraimpala.apache.orgfauna.comazure.microsoft.com/­services/­data-explorerwww.yugabyte.com
Technical documentationdocs.aws.amazon.com/­AmazonRDS/­latest/­AuroraUserGuide/­CHAP_Aurora.htmlimpala.apache.org/­impala-docs.htmldocs.fauna.comdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.yugabyte.com
github.com/­yugabyte/­yugabyte-db
DeveloperAmazonApache Software Foundation infoApache top-level project, originally developed by ClouderaFauna, Inc.MicrosoftYugabyte Inc.
Initial release20152013201420192017
Current release4.1.0, June 2022cloud service with continuous releases2.19, September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2commercialcommercialOpen Source infoApache 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.
YugabyteDB Managed is the fully managed database-as-a-service offering of YugabyteDB. Get started quickly, and effortlessly ensure continuous availability and limitless scale of your cloud native applications.
Implementation languageC++ScalaC and C++
Server operating systemshostedLinuxhostedhostedLinux
OS X
Data schemeyesyesschema-freeFixed schema with schema-less datatypes (dynamic)depending on used data model
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
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 indexesyesyesyesall fields are automatically indexedyes
SQL infoSupport of SQLyesSQL-like DML and DDL statementsnoKusto Query Language (KQL), SQL subsetyes, PostgreSQL compatible
APIs and other access methodsADO.NET
JDBC
ODBC
JDBC
ODBC
RESTful HTTP APIMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
JDBC
YCQL, an SQL-based flexible-schema API with its roots in Cassandra Query Language
YSQL - a fully relational SQL API that is wire compatible with the SQL language in PostgreSQL
Supported programming languagesAda
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
All languages supporting JDBC/ODBCC#
Go
Java
JavaScript
Python
Ruby
Scala
Swift
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust
Scala
Server-side scripts infoStored proceduresyesyes infouser defined functions and integration of map-reduceuser defined functionsYes, possible languages: KQL, Python, Ryes infosql, plpgsql, C
Triggersyesnonoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningShardinghorizontal partitioning infoconsistent hashingSharding infoImplicit feature of the cloud serviceHash and Range Sharding, row-level geo-partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationselectable replication factorMulti-source replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Based on Raft distributed consensus protocol, minimum 3 replicas for continuous availability
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
Strong consistency on writes and tunable consistency on reads
Foreign keys infoReferential integrityyesnoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDnoDistributed ACID with Serializable & Snapshot Isolation. Inspired by Google Spanner architecture.
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes infobased on RocksDB
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnononono
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users, groups and roles infobased on Apache Sentry and KerberosIdentity management, authentication, and access controlAzure Active Directory Authenticationyes
More information provided by the system vendor
Amazon AuroraApache ImpalaFauna infopreviously named FaunaDBMicrosoft Azure Data ExplorerYugabyteDB
Specific characteristicsYugabyteDB is an open source distributed SQL database for cloud native transactional...
» more
Competitive advantagesPostgreSQL compatible: Get instantly productive with a PostgreSQL compatible RDBMS....
» more
Typical application scenariosSystems of record and engagement for cloud native applications that require resilience,...
» more
Market metrics2 Million+ lifetime clusters deployed, 6.5K+ GitHub stars, 7K YugabyteDB Community...
» more
Licensing and pricing modelsApache 2.0 license for the database
» more

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More resources
Amazon AuroraApache ImpalaFauna infopreviously named FaunaDBMicrosoft Azure Data ExplorerYugabyteDB
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