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DBMS > Ehcache vs. HEAVY.AI vs. Microsoft Azure Data Explorer vs. Tibero vs. Weaviate

System Properties Comparison Ehcache vs. HEAVY.AI vs. Microsoft Azure Data Explorer vs. Tibero vs. Weaviate

Editorial information provided by DB-Engines
NameEhcache  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonTibero  Xexclude from comparisonWeaviate  Xexclude from comparison
DescriptionA widely adopted Java cache with tiered storage optionsA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareFully managed big data interactive analytics platformA secure RDBMS, designed for easy portability from OracleAn AI-native realtime vector database engine that integrates scalable machine learning models.
Primary database modelKey-value storeRelational DBMSRelational DBMS infocolumn orientedRelational DBMSVector DBMS
Secondary database modelsSpatial DBMSDocument 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
Score4.89
Rank#67  Overall
#8  Key-value stores
Score1.77
Rank#141  Overall
#65  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score1.78
Rank#140  Overall
#64  Relational DBMS
Score1.73
Rank#143  Overall
#5  Vector DBMS
Websitewww.ehcache.orggithub.com/­heavyai/­heavydb
www.heavy.ai
azure.microsoft.com/­services/­data-explorerus.tmaxsoft.com/­products/­tiberogithub.com/­weaviate/­weaviate
weaviate.io
Technical documentationwww.ehcache.org/­documentationdocs.heavy.aidocs.microsoft.com/­en-us/­azure/­data-explorertechnet.tmaxsoft.com/­upload/­download/­online/­tibero/­pver-20150504-000002/­index.htmlweaviate.io/­developers/­weaviate
DeveloperTerracotta Inc, owned by Software AGHEAVY.AI, Inc.MicrosoftTmaxSoftWeaviate B.V.
Initial release20092016201920032019
Current release3.10.0, March 20225.10, January 2022cloud service with continuous releases6, April 20151.19, May 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2; commercial licenses availableOpen Source infoApache Version 2; enterprise edition availablecommercialcommercialOpen Source infocommercial license available with Weaviate Enterprise
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++ and CUDAC and AssemblerGo
Server operating systemsAll OS with a Java VMLinuxhostedAIX
HP-UX
Linux
Solaris
Windows
Data schemeschema-freeyesFixed schema with schema-less datatypes (dynamic)yesyes, maps to GraphQL interface
Typing infopredefined data types such as float or dateyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyesyes infostring, int, float, geo point, date, cross reference, fuzzy references
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.nonoyesyesno
Secondary indexesnonoall fields are automatically indexedyesyes infoall data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed
SQL infoSupport of SQLnoyesKusto Query Language (KQL), SQL subsetyesGraphQL is used as query language
APIs and other access methodsJCacheJDBC
ODBC
Thrift
Vega
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI
GraphQL query language
RESTful HTTP/JSON API
Supported programming languagesJavaAll languages supporting JDBC/ODBC/Thrift
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic
JavaScript / TypeScript
Python
Server-side scripts infoStored proceduresnonoYes, possible languages: KQL, Python, RPersistent Stored Procedure (PSM)no
Triggersyes infoCache Event Listenersnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyesno
Partitioning methods infoMethods for storing different data on different nodesSharding infoby using Terracotta ServerSharding infoRound robinSharding infoImplicit feature of the cloud servicehorizontal partitioning infoby range, hash, list or compositeSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoby using Terracotta ServerMulti-source replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Multi-source replication
Source-replica replication
yes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoSpark connector (open source): github.com/­Azure/­azure-kusto-sparknono
Consistency concepts infoMethods to ensure consistency in a distributed systemTunable Consistency (Strong, Eventual, Weak)Immediate ConsistencyEventual Consistency
Immediate Consistency
Immediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynononoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayes infosupports JTA and can work as an XA resourcenonoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyes infousing a tiered cache-storage approachyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesnono infoplanned for next versionyes
User concepts infoAccess controlnofine grained access rights according to SQL-standardAzure Active Directory Authenticationfine grained access rights according to SQL-standard (SQL 92, SQL 99)API Keys
OpenID Connect Discovery
More information provided by the system vendor
EhcacheHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Microsoft Azure Data ExplorerTiberoWeaviate
Specific characteristicsWeaviate is an open source vector database that is robust, scalable, cloud-native,...
» more
Competitive advantagesFlexible deployment - Free, open source or fully-managed cloud vector database service...
» more
Typical application scenariosAs a database supporting the development of generative AI and semantic search applications...
» more
Key customersAll companies that have data. ​
» more
Market metricsAs of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
» more
Licensing and pricing modelsWeaviate is open-source, and free to use. Weaviate is also available as a fully managed...
» more

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EhcacheHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Microsoft Azure Data ExplorerTiberoWeaviate
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