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DBMS > Faircom EDGE vs. Hazelcast vs. Ingres vs. Microsoft Azure Data Explorer vs. XTDB

System Properties Comparison Faircom EDGE vs. Hazelcast vs. Ingres vs. Microsoft Azure Data Explorer vs. XTDB

Editorial information provided by DB-Engines
NameFaircom EDGE infoformerly c-treeEDGE  Xexclude from comparisonHazelcast  Xexclude from comparisonIngres  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonXTDB infoformerly named Crux  Xexclude from comparison
DescriptionFairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environmentsA widely adopted in-memory data gridWell established RDBMSFully managed big data interactive analytics platformA general purpose database with bitemporal SQL and Datalog and graph queries
Primary database modelKey-value store
Relational DBMS
Key-value storeRelational DBMSRelational DBMS infocolumn orientedDocument store
Secondary database modelsDocument store infoJSON support with IMDG 3.12Document 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
Score0.09
Rank#362  Overall
#53  Key-value stores
#155  Relational DBMS
Score5.46
Rank#61  Overall
#7  Key-value stores
Score3.80
Rank#82  Overall
#44  Relational DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score0.18
Rank#332  Overall
#46  Document stores
Websitewww.faircom.com/­products/­faircom-edgehazelcast.comwww.actian.com/­databases/­ingresazure.microsoft.com/­services/­data-explorergithub.com/­xtdb/­xtdb
www.xtdb.com
Technical documentationdocs.faircom.com/­docs/­en/­UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.htmlhazelcast.org/­imdg/­docsdocs.actian.com/­ingresdocs.microsoft.com/­en-us/­azure/­data-explorerwww.xtdb.com/­docs
DeveloperFairCom CorporationHazelcastActian CorporationMicrosoftJuxt Ltd.
Initial release197920081974 infooriginally developed at University Berkely in early 1970s20192019
Current releaseV3, October 20205.3.6, November 202311.2, May 2022cloud service with continuous releases1.19, September 2021
License infoCommercial or Open Sourcecommercial infoRestricted, free version availableOpen Source infoApache Version 2; commercial licenses availablecommercialcommercialOpen Source infoMIT License
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 languageANSI C, C++JavaCClojure
Server operating systemsAndroid
Linux infoARM, x86
Raspbian
Windows
All OS with a Java VMAIX
HP Open VMS
HP-UX
Linux
Solaris
Windows
hostedAll OS with a Java 8 (and higher) VM
Linux
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeyesFixed schema with schema-less datatypes (dynamic)schema-free
Typing infopredefined data types such as float or dateyes, ANSI Standard SQL Typesyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes, extensible-data-notation format
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 infothe object must implement a serialization strategyno infobut tools for importing/exporting data from/to XML-files availableyesno
Secondary indexesyesyesyesall fields are automatically indexedyes
SQL infoSupport of SQLyes infoANSI SQL queriesSQL-like query languageyesKusto Query Language (KQL), SQL subsetlimited SQL, making use of Apache Calcite
APIs and other access methodsADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API
JCache
JPA
Memcached protocol
RESTful HTTP API
.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
HTTP REST
JDBC
Supported programming languagesC
C#
C++
Java
JavaScript
PHP
Python
VB.Net
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Clojure
Java
Server-side scripts infoStored proceduresyes info.Net, JavaScript, C/C++yes infoEvent Listeners, Executor ServicesyesYes, possible languages: KQL, Python, Rno
Triggersyesyes infoEventsyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodesFile partitioning infoCustomizable business rules for partitioningShardinghorizontal partitioning infoIngres Star to access multiple databases simultaneouslySharding infoImplicit feature of the cloud servicenone
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoSynchronous and asynchronous realtime replication based on transaction logsyes infoReplicated MapIngres Replicatoryes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes, each node contains all data
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesnoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency
Tunable Consistency
Immediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityyes infowhen using SQLnoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDone or two-phase-commit; repeatable reads; read commitedACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayes infoacross SQL and NoSQLyesyes infoMVCCyesyes
Durability infoSupport for making data persistentyesyesyesyesyes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesnono
User concepts infoAccess controlFine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.Role-based access controlfine grained access rights according to SQL-standardAzure Active Directory Authentication

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More resources
Faircom EDGE infoformerly c-treeEDGEHazelcastIngresMicrosoft Azure Data ExplorerXTDB infoformerly named Crux
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