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DBMS > Amazon Aurora vs. Databricks vs. Fauna

System Properties Comparison Amazon Aurora vs. Databricks vs. Fauna

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Editorial information provided by DB-Engines
NameAmazon Aurora  Xexclude from comparisonDatabricks  Xexclude from comparisonFauna infopreviously named FaunaDB  Xexclude from comparison
DescriptionMySQL and PostgreSQL compatible cloud service by AmazonThe Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.Fauna 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.
Primary database modelRelational DBMSDocument store
Relational DBMS
Document store
Graph DBMS
Relational DBMS
Time Series DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score7.91
Rank#50  Overall
#32  Relational DBMS
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score1.52
Rank#153  Overall
#26  Document stores
#14  Graph DBMS
#71  Relational DBMS
#13  Time Series DBMS
Websiteaws.amazon.com/­rds/­aurorawww.databricks.comfauna.com
Technical documentationdocs.aws.amazon.com/­AmazonRDS/­latest/­AuroraUserGuide/­CHAP_Aurora.htmldocs.databricks.comdocs.fauna.com
DeveloperAmazonDatabricksFauna, Inc.
Initial release201520132014
License infoCommercial or Open Sourcecommercialcommercialcommercial
Cloud-based only infoOnly available as a cloud serviceyesyesyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScala
Server operating systemshostedhostedhosted
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)schema-free
Typing infopredefined data types such as float or dateyesno
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.yesyesno
Secondary indexesyesyesyes
SQL infoSupport of SQLyeswith Databricks SQLno
APIs and other access methodsADO.NET
JDBC
ODBC
JDBC
ODBC
RESTful HTTP API
RESTful HTTP API
Supported programming languagesAda
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
Python
R
Scala
C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift
Server-side scripts infoStored proceduresyesuser defined functions and aggregatesuser defined functions
Triggersyesno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioninghorizontal partitioning infoconsistent hashing
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyesMulti-source replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnono
User concepts infoAccess controlfine grained access rights according to SQL-standardIdentity management, authentication, and access control
More information provided by the system vendor
Amazon AuroraDatabricksFauna infopreviously named FaunaDB
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
Amazon AuroraDatabricksFauna infopreviously named FaunaDB
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