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DBMS > Microsoft Azure Data Explorer vs. Oracle Berkeley DB vs. Titan vs. Trafodion vs. YDB

System Properties Comparison Microsoft Azure Data Explorer vs. Oracle Berkeley DB vs. Titan vs. Trafodion vs. YDB

Editorial information provided by DB-Engines
NameMicrosoft Azure Data Explorer  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparisonTitan  Xexclude from comparisonTrafodion  Xexclude from comparisonYDB  Xexclude from comparison
Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.
DescriptionFully managed big data interactive analytics platformWidely used in-process key-value storeTitan is a Graph DBMS optimized for distributed clusters.Transactional SQL-on-Hadoop DBMSA distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API
Primary database modelRelational DBMS infocolumn orientedKey-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
Graph DBMSRelational DBMSDocument store
Relational DBMS
Secondary database modelsDocument 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
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score2.01
Rank#126  Overall
#21  Key-value stores
#3  Native XML DBMS
Score0.33
Rank#287  Overall
#43  Document stores
#132  Relational DBMS
Websiteazure.microsoft.com/­services/­data-explorerwww.oracle.com/­database/­technologies/­related/­berkeleydb.htmlgithub.com/­thinkaurelius/­titantrafodion.apache.orggithub.com/­ydb-platform/­ydb
ydb.tech
Technical documentationdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.oracle.com/­cd/­E17076_05/­html/­index.htmlgithub.com/­thinkaurelius/­titan/­wikitrafodion.apache.org/­documentation.htmlydb.tech/­en/­docs
DeveloperMicrosoftOracle infooriginally developed by Sleepycat, which was acquired by OracleAurelius, owned by DataStaxApache Software Foundation, originally developed by HPYandex
Initial release20191994201220142019
Current releasecloud service with continuous releases18.1.40, May 20202.3.0, February 2019
License infoCommercial or Open SourcecommercialOpen Source infocommercial license availableOpen Source infoApache license, version 2.0Open Source infoApache 2.0Open Source infoApache 2.0; commercial license available
Cloud-based only infoOnly available as a cloud serviceyesnononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, Java, C++ (depending on the Berkeley DB edition)JavaC++, Java
Server operating systemshostedAIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
Linux
OS X
Unix
Windows
LinuxLinux
Data schemeFixed schema with schema-less datatypes (dynamic)schema-freeyesyesFlexible Schema (defined schema, partial schema, schema free)
Typing infopredefined data types such as float or dateyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesnoyesyesyes
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.yesyes infoonly with the Berkeley DB XML editionnono
Secondary indexesall fields are automatically indexedyesyesyesyes
SQL infoSupport of SQLKusto Query Language (KQL), SQL subsetyes infoSQL interfaced based on SQLite is availablenoyesSQL-like query language (YQL)
APIs and other access methodsMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster
ADO.NET
JDBC
ODBC
RESTful HTTP API (DynamoDB compatible)
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net infoFigaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others infoThird-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js) info3rd party binding
Perl
Python
Tcl
Clojure
Java
Python
All languages supporting JDBC/ODBC/ADO.NetGo
Java
JavaScript (Node.js)
PHP
Python
Server-side scripts infoStored proceduresYes, possible languages: KQL, Python, RnoyesJava Stored Proceduresno
Triggersyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infoonly for the SQL APIyesno
Partitioning methods infoMethods for storing different data on different nodesSharding infoImplicit feature of the cloud servicenoneyes infovia pluggable storage backendsShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replicationyesyes, via HBaseActive-passive shard replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsSpark connector (open source): github.com/­Azure/­azure-kusto-sparknoyes infovia Faunus, a graph analytics engineyes infovia user defined functions and HBaseno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Eventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyes infoRelationships in graphyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes infoSupports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcastyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesnono
User concepts infoAccess controlAzure Active Directory AuthenticationnoUser authentification and security via Rexster Graph Serverfine grained access rights according to SQL-standardAccess rights defined for Yandex Cloud users

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More resources
Microsoft Azure Data ExplorerOracle Berkeley DBTitanTrafodionYDB
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