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DBMS > Amazon Aurora vs. Apache Impala vs. Atos Standard Common Repository vs. Drizzle vs. Google Cloud Datastore

System Properties Comparison Amazon Aurora vs. Apache Impala vs. Atos Standard Common Repository vs. Drizzle vs. Google Cloud Datastore

Editorial information provided by DB-Engines
NameAmazon Aurora  Xexclude from comparisonApache Impala  Xexclude from comparisonAtos Standard Common Repository  Xexclude from comparisonDrizzle  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.
DescriptionMySQL and PostgreSQL compatible cloud service by AmazonAnalytic DBMS for HadoopHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform
Primary database modelRelational DBMSRelational DBMSDocument store
Key-value store
Relational DBMSDocument store
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
Score4.36
Rank#72  Overall
#12  Document stores
Websiteaws.amazon.com/­rds/­auroraimpala.apache.orgatos.net/en/convergence-creators/portfolio/standard-common-repositorycloud.google.com/­datastore
Technical documentationdocs.aws.amazon.com/­AmazonRDS/­latest/­AuroraUserGuide/­CHAP_Aurora.htmlimpala.apache.org/­impala-docs.htmlcloud.google.com/­datastore/­docs
DeveloperAmazonApache Software Foundation infoApache top-level project, originally developed by ClouderaAtos Convergence CreatorsDrizzle project, originally started by Brian AkerGoogle
Initial release20152013201620082008
Current release4.1.0, June 202217037.2.4, September 2012
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2commercialOpen Source infoGNU GPLcommercial
Cloud-based only infoOnly available as a cloud serviceyesnononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++JavaC++
Server operating systemshostedLinuxLinuxFreeBSD
Linux
OS X
hosted
Data schemeyesyesSchema and schema-less with LDAP viewsyesschema-free
Typing infopredefined data types such as float or dateyesyesoptionalyesyes, details here
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 statementsnoyes infowith proprietary extensionsSQL-like query language (GQL)
APIs and other access methodsADO.NET
JDBC
ODBC
JDBC
ODBC
LDAPJDBCgRPC (using protocol buffers) API
RESTful HTTP/JSON 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
All languages supporting JDBC/ODBCAll languages with LDAP bindingsC
C++
Java
PHP
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresyesyes infouser defined functions and integration of map-reducenonousing Google App Engine
Triggersyesnoyesno infohooks for callbacks inside the server can be used.Callbacks using the Google Apps Engine
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningShardingSharding infocell divisionShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationselectable replication factoryesMulti-source replication
Source-replica replication
Multi-source replication using Paxos
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infoquery execution via MapReducenoyes infousing Google Cloud Dataflow
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.
Foreign keys infoReferential integrityyesnonoyesyes infovia ReferenceProperties or Ancestor paths
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoAtomic execution of specific operationsACIDACID infoSerializable Isolation within Transactions, Read Committed outside of Transactions
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.yesnoyesno
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users, groups and roles infobased on Apache Sentry and KerberosLDAP bind authenticationPluggable authentication mechanisms infoe.g. LDAP, HTTPAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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More resources
Amazon AuroraApache ImpalaAtos Standard Common RepositoryDrizzleGoogle Cloud Datastore
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