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DBMS > Apache Druid vs. Faircom DB vs. Faircom EDGE vs. Microsoft Azure Data Explorer vs. SwayDB

System Properties Comparison Apache Druid vs. Faircom DB vs. Faircom EDGE vs. Microsoft Azure Data Explorer vs. SwayDB

Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonFaircom DB infoformerly c-treeACE  Xexclude from comparisonFaircom EDGE infoformerly c-treeEDGE  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSwayDB  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataNative high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environmentsFully managed big data interactive analytics platformAn embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage
Primary database modelRelational DBMS
Time Series DBMS
Key-value store
Relational DBMS
Key-value store
Relational DBMS
Relational DBMS infocolumn orientedKey-value store
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
Score3.25
Rank#90  Overall
#47  Relational DBMS
#7  Time Series DBMS
Score0.29
Rank#304  Overall
#43  Key-value stores
#136  Relational DBMS
Score0.09
Rank#362  Overall
#53  Key-value stores
#155  Relational DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score0.04
Rank#387  Overall
#61  Key-value stores
Websitedruid.apache.orgwww.faircom.com/­products/­faircom-dbwww.faircom.com/­products/­faircom-edgeazure.microsoft.com/­services/­data-explorerswaydb.simer.au
Technical documentationdruid.apache.org/­docs/­latest/­designdocs.faircom.com/­docs/­en/­UUID-7446ae34-a1a7-c843-c894-d5322e395184.htmldocs.faircom.com/­docs/­en/­UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.htmldocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperApache Software Foundation and contributorsFairCom CorporationFairCom CorporationMicrosoftSimer Plaha
Initial release20121979197920192018
Current release29.0.1, April 2024V12, November 2020V3, October 2020cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache license v2commercial infoRestricted, free version availablecommercial infoRestricted, free version availablecommercialOpen Source infoGNU Affero GPL V3.0
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaANSI C, C++ANSI C, C++Scala
Server operating systemsLinux
OS X
Unix
AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows infoeasily portable to other OSs
Android
Linux infoARM, x86
Raspbian
Windows
hosted
Data schemeyes infoschema-less columns are supportedschema free, schema optional, schema required, partial schema,Flexible Schema (defined schema, partial schema, schema free)Fixed schema with schema-less datatypes (dynamic)schema-free
Typing infopredefined data types such as float or dateyesyes, ANSI SQL Types, JSON, typed binary structuresyes, ANSI Standard SQL Typesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesno
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 indexesyesyesyesall fields are automatically indexedno
SQL infoSupport of SQLSQL for queryingyes, ANSI SQL with proprietary extensionsyes infoANSI SQL queriesKusto Query Language (KQL), SQL subsetno
APIs and other access methodsJDBC
RESTful HTTP/JSON API
ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC
ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic
C
C#
C++
Java
JavaScript
PHP
Python
VB.Net
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Java
Kotlin
Scala
Server-side scripts infoStored proceduresnoyes info.Net, JavaScript, C/C++yes info.Net, JavaScript, C/C++Yes, possible languages: KQL, Python, Rno
Triggersnoyesyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedFile partitioning, horizontal partitioning, sharding infoCustomizable business rules for table partitioningFile partitioning infoCustomizable business rules for partitioningSharding infoImplicit feature of the cloud servicenone
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesyes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).yes infoSynchronous and asynchronous realtime replication based on transaction logsyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.none
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 ConsistencyEventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction
Immediate Consistency
Tunable Consistency
Eventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyesyes infowhen using SQLnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanotunable from ACID to Eventually ConsistentACIDnoAtomic execution of operations
Concurrency infoSupport for concurrent manipulation of datayesyesyes infoacross SQL and NoSQLyesyes
Durability infoSupport for making data persistentyesYes, tunable from durable to delayed durability to in-memoryyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyesnoyes
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemFine grained access rights according to SQL-standard with additional protections for filesFine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.Azure Active Directory Authenticationno

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More resources
Apache DruidFaircom DB infoformerly c-treeACEFaircom EDGE infoformerly c-treeEDGEMicrosoft Azure Data ExplorerSwayDB
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