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DBMS > Apache Druid vs. eXtremeDB vs. Heroic vs. Microsoft Azure Data Explorer vs. Weaviate

System Properties Comparison Apache Druid vs. eXtremeDB vs. Heroic vs. Microsoft Azure Data Explorer vs. Weaviate

Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisoneXtremeDB  Xexclude from comparisonHeroic  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonWeaviate  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataNatively in-memory DBMS with options for persistency, high-availability and clusteringTime Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchFully managed big data interactive analytics platformAn AI-native realtime vector database engine that integrates scalable machine learning models.
Primary database modelRelational DBMS
Time Series DBMS
Relational DBMS
Time Series DBMS
Time Series DBMSRelational DBMS infocolumn orientedVector 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
Score3.34
Rank#88  Overall
#48  Relational DBMS
#7  Time Series DBMS
Score0.74
Rank#223  Overall
#103  Relational DBMS
#18  Time Series DBMS
Score0.51
Rank#255  Overall
#21  Time Series DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score1.73
Rank#143  Overall
#5  Vector DBMS
Websitedruid.apache.orgwww.mcobject.comgithub.com/­spotify/­heroicazure.microsoft.com/­services/­data-explorergithub.com/­weaviate/­weaviate
weaviate.io
Technical documentationdruid.apache.org/­docs/­latest/­designwww.mcobject.com/­docs/­extremedb.htmspotify.github.io/­heroicdocs.microsoft.com/­en-us/­azure/­data-explorerweaviate.io/­developers/­weaviate
DeveloperApache Software Foundation and contributorsMcObjectSpotifyMicrosoftWeaviate B.V.
Initial release20122001201420192019
Current release29.0.1, April 20248.2, 2021cloud service with continuous releases1.19, May 2023
License infoCommercial or Open SourceOpen Source infoApache license v2commercialOpen Source infoApache 2.0commercialOpen Source infocommercial license available with Weaviate Enterprise
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC and C++JavaGo
Server operating systemsLinux
OS X
Unix
AIX
HP-UX
Linux
macOS
Solaris
Windows
hosted
Data schemeyes infoschema-less columns are supportedyesschema-freeFixed schema with schema-less datatypes (dynamic)yes, maps to GraphQL interface
Typing infopredefined data types such as float or dateyesyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes 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.nono infosupport of XML interfaces availablenoyesno
Secondary indexesyesyesyes infovia Elasticsearchall fields are automatically indexedyes infoall data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed
SQL infoSupport of SQLSQL for queryingyes infowith the option: eXtremeSQLnoKusto Query Language (KQL), SQL subsetGraphQL is used as query language
APIs and other access methodsJDBC
RESTful HTTP/JSON API
.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API
HQL (Heroic Query Language, a JSON-based language)
HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
GraphQL query language
RESTful HTTP/JSON API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
.Net
C
C#
C++
Java
Lua
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
JavaScript / TypeScript
Python
Server-side scripts infoStored proceduresnoyesnoYes, possible languages: KQL, Python, Rno
Triggersnoyes infoby defining eventsnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedhorizontal partitioning / shardingShardingSharding infoImplicit feature of the cloud serviceSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesActive Replication Fabricâ„¢ for IoT
Multi-source replication infoby means of eXtremeDB Cluster option
Source-replica replication infoby means of eXtremeDB High Availability option
yesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Eventual Consistency
Immediate Consistency
Eventual Consistency
Foreign keys infoReferential integritynoyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnonono
Concurrency infoSupport for concurrent manipulation of datayesyes infoOptimistic (MVCC) and pessimistic (locking) strategies availableyesyesyes
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.noyesnonoyes
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemAzure Active Directory AuthenticationAPI Keys
OpenID Connect Discovery
More information provided by the system vendor
Apache DruideXtremeDBHeroicMicrosoft Azure Data ExplorerWeaviate
Specific characteristicseXtremeDB is an in-memory and/or persistent database system that offers an ultra-small...
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Weaviate is an open source vector database that is robust, scalable, cloud-native,...
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Competitive advantageseXtremeDB databases can be modeled relationally or as objects and can utilize SQL...
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Flexible deployment - Free, open source or fully-managed cloud vector database service...
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Typical application scenariosIoT application across all markets: Industrial Control, Netcom, Telecom, Defense,...
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As a database supporting the development of generative AI and semantic search applications...
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Key customersSchneider Electronics, F5 Networks, TNS, Boeing, Northrop Grumman, GoPro, ViaSat,...
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All companies that have data. ​
» more
Market metricsWith hundreds of customers and over 30 million devices/applications using the product...
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As of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
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Licensing and pricing modelsFor server use cases, there is a simple per-server license irrespective of the number...
» more
Weaviate is open-source, and free to use. Weaviate is also available as a fully managed...
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More resources
Apache DruideXtremeDBHeroicMicrosoft Azure Data ExplorerWeaviate
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