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DBMS > EventStoreDB vs. Google Cloud Datastore vs. Hypertable vs. Microsoft Azure Synapse Analytics vs. Percona Server for MongoDB

System Properties Comparison EventStoreDB vs. Google Cloud Datastore vs. Hypertable vs. Microsoft Azure Synapse Analytics vs. Percona Server for MongoDB

Editorial information provided by DB-Engines
NameEventStoreDB  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonHypertable  Xexclude from comparisonMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data Warehouse  Xexclude from comparisonPercona Server for MongoDB  Xexclude from comparison
Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.
DescriptionIndustrial-strength, open-source database solution built from the ground up for event sourcing.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformAn open source BigTable implementation based on distributed file systems such as HadoopElastic, large scale data warehouse service leveraging the broad eco-system of SQL ServerA drop-in replacement for MongoDB Community Edition with enterprise-grade features.
Primary database modelEvent StoreDocument storeWide column storeRelational DBMSDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.10
Rank#179  Overall
#1  Event Stores
Score4.47
Rank#76  Overall
#12  Document stores
Score20.56
Rank#31  Overall
#19  Relational DBMS
Score0.52
Rank#254  Overall
#39  Document stores
Websitewww.eventstore.comcloud.google.com/­datastoreazure.microsoft.com/­services/­synapse-analyticswww.percona.com/­mongodb/­software/­percona-server-for-mongodb
Technical documentationdevelopers.eventstore.comcloud.google.com/­datastore/­docsdocs.microsoft.com/­azure/­synapse-analyticsdocs.percona.com/­percona-distribution-for-mongodb
DeveloperEvent Store LimitedGoogleHypertable Inc.MicrosoftPercona
Initial release20122008200920162015
Current release21.2, February 20210.9.8.11, March 20163.4.10-2.10, November 2017
License infoCommercial or Open SourceOpen SourcecommercialOpen Source infoGNU version 3. Commercial license availablecommercialOpen Source infoGPL Version 2
Cloud-based only infoOnly available as a cloud servicenoyesnoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++C++C++
Server operating systemsLinux
Windows
hostedLinux
OS X
Windows infoan inofficial Windows port is available
hostedLinux
Data schemeschema-freeschema-freeyesschema-free
Typing infopredefined data types such as float or dateyes, details herenoyesyes
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.nonono
Secondary indexesyesrestricted infoonly exact value or prefix value scansyesyes
SQL infoSupport of SQLSQL-like query language (GQL)noyesno
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
C++ API
Thrift
ADO.NET
JDBC
ODBC
proprietary protocol using JSON
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C++
Java
Perl
PHP
Python
Ruby
C#
Java
PHP
Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk
Server-side scripts infoStored proceduresusing Google App EnginenoTransact SQLJavaScript
TriggersCallbacks using the Google Apps Enginenonono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding, horizontal partitioningSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using Paxosselectable replication factor on file system levelyesSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflowyesnoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate 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.Immediate ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnono infodocs.microsoft.com/­en-us/­azure/­synapse-analytics/­sql-data-warehouse/­sql-data-warehouse-table-constraintsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infovia In-Memory Engine
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)noyesAccess rights for users and roles

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More resources
EventStoreDBGoogle Cloud DatastoreHypertableMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data WarehousePercona Server for MongoDB
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