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DBMS > Amazon Aurora vs. Apache Impala vs. Databricks vs. FatDB vs. JaguarDB

System Properties Comparison Amazon Aurora vs. Apache Impala vs. Databricks vs. FatDB vs. JaguarDB

Editorial information provided by DB-Engines
NameAmazon Aurora  Xexclude from comparisonApache Impala  Xexclude from comparisonDatabricks  Xexclude from comparisonFatDB  Xexclude from comparisonJaguarDB  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
DescriptionMySQL and PostgreSQL compatible cloud service by AmazonAnalytic DBMS for HadoopThe 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.A .NET NoSQL DBMS that can integrate with and extend SQL Server.Performant, highly scalable DBMS for AI and IoT applications
Primary database modelRelational DBMSRelational DBMSDocument store
Relational DBMS
Document store
Key-value store
Key-value store
Vector DBMS
Secondary database modelsDocument storeDocument 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
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score0.06
Rank#381  Overall
#59  Key-value stores
#13  Vector DBMS
Websiteaws.amazon.com/­rds/­auroraimpala.apache.orgwww.databricks.comwww.jaguardb.com
Technical documentationdocs.aws.amazon.com/­AmazonRDS/­latest/­AuroraUserGuide/­CHAP_Aurora.htmlimpala.apache.org/­impala-docs.htmldocs.databricks.comwww.jaguardb.com/­support.html
DeveloperAmazonApache Software Foundation infoApache top-level project, originally developed by ClouderaDatabricksFatCloudDataJaguar, Inc.
Initial release20152013201320122015
Current release4.1.0, June 20223.3 July 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2commercialcommercialOpen Source infoGPL V3.0
Cloud-based only infoOnly available as a cloud serviceyesnoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++C#C++ infothe server part. Clients available in other languages
Server operating systemshostedLinuxhostedWindowsLinux
Data schemeyesyesFlexible Schema (defined schema, partial schema, schema free)schema-freeyes
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.yesnoyesno
Secondary indexesyesyesyesyesyes
SQL infoSupport of SQLyesSQL-like DML and DDL statementswith Databricks SQLno infoVia inetgration in SQL ServerA subset of ANSI SQL is implemented infobut no views, foreign keys, triggers
APIs and other access methodsADO.NET
JDBC
ODBC
JDBC
ODBC
JDBC
ODBC
RESTful HTTP API
.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
JDBC
ODBC
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/ODBCPython
R
Scala
C#C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresyesyes infouser defined functions and integration of map-reduceuser defined functions and aggregatesyes infovia applicationsno
Triggersyesnoyes infovia applicationsno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationselectable replication factoryesselectable replication factorMulti-source replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infoquery execution via MapReduceyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Eventual Consistency
Foreign keys infoReferential integrityyesnonono
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 persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnonono
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosno infoCan implement custom security layer via applicationsrights management via user accounts
More information provided by the system vendor
Amazon AuroraApache ImpalaDatabricksFatDBJaguarDB
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
Amazon AuroraApache ImpalaDatabricksFatDBJaguarDB
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