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DBMS > Apache Druid vs. Elasticsearch vs. Faircom EDGE vs. GeoSpock vs. Microsoft Azure Data Explorer

System Properties Comparison Apache Druid vs. Elasticsearch vs. Faircom EDGE vs. GeoSpock vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonElasticsearch  Xexclude from comparisonFaircom EDGE infoformerly c-treeEDGE  Xexclude from comparisonGeoSpock  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataA distributed, RESTful modern search and analytics engine based on Apache Lucene infoElasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metricFairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environmentsSpatial and temporal data processing engine for extreme data scaleFully managed big data interactive analytics platform
Primary database modelRelational DBMS
Time Series DBMS
Search engineKey-value store
Relational DBMS
Relational DBMSRelational DBMS infocolumn oriented
Secondary database modelsDocument store
Spatial DBMS
Vector DBMS
Time Series 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
Score2.85
Rank#96  Overall
#50  Relational DBMS
#6  Time Series DBMS
Score128.79
Rank#8  Overall
#1  Search engines
Score0.02
Rank#370  Overall
#55  Key-value stores
#157  Relational DBMS
Score3.28
Rank#83  Overall
#45  Relational DBMS
Websitedruid.apache.orgwww.elastic.co/­elasticsearchwww.faircom.com/­products/­faircom-edgegeospock.comazure.microsoft.com/­services/­data-explorer
Technical documentationdruid.apache.org/­docs/­latest/­designwww.elastic.co/­guide/­en/­elasticsearch/­reference/­current/­index.htmldocs.faircom.com/­docs/­en/­UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.htmldocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperApache Software Foundation and contributorsElasticFairCom CorporationGeoSpockMicrosoft
Initial release2012201019792019
Current release30.0.0, June 20248.6, January 2023V3, October 20202.0, September 2019cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache license v2Open Source infoElastic Licensecommercial infoRestricted, free version availablecommercialcommercial
Cloud-based only infoOnly available as a cloud servicenononoyesyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaANSI C, C++Java, Javascript
Server operating systemsLinux
OS X
Unix
All OS with a Java VMAndroid
Linux infoARM, x86
Raspbian
Windows
hostedhosted
Data schemeyes infoschema-less columns are supportedschema-free infoFlexible type definitions. Once a type is defined, it is persistentFlexible Schema (defined schema, partial schema, schema free)yesFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesyesyes, ANSI Standard SQL Typesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types
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.nonoyesnoyes
Secondary indexesyesyes infoAll search fields are automatically indexedyestemporal, categoricalall fields are automatically indexed
SQL infoSupport of SQLSQL for queryingSQL-like query languageyes infoANSI SQL queriesANSI SQL for query only (using Presto)Kusto Query Language (KQL), SQL subset
APIs and other access methodsJDBC
RESTful HTTP/JSON API
Java API
RESTful HTTP/JSON API
ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API
JDBCMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby
C
C#
C++
Java
JavaScript
PHP
Python
VB.Net
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresnoyesyes info.Net, JavaScript, C/C++noYes, possible languages: KQL, Python, R
Triggersnoyes infoby using the 'percolation' featureyesnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingFile partitioning infoCustomizable business rules for partitioningAutomatic shardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesyesyes infoSynchronous and asynchronous realtime replication based on transaction logsyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoES-Hadoop ConnectornonoSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency infoSynchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, allImmediate Consistency
Tunable Consistency
Immediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynonoyes infowhen using SQLnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyes infoacross SQL and NoSQLyesyes
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.noMemcached and Redis integrationyesnono
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemFine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.Access rights for users can be defined per tableAzure Active Directory Authentication

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More resources
Apache DruidElasticsearchFaircom EDGE infoformerly c-treeEDGEGeoSpockMicrosoft Azure Data Explorer
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