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DBMS > Faircom EDGE vs. Hawkular Metrics vs. Ingres vs. Microsoft Azure Synapse Analytics vs. Splice Machine

System Properties Comparison Faircom EDGE vs. Hawkular Metrics vs. Ingres vs. Microsoft Azure Synapse Analytics vs. Splice Machine

Editorial information provided by DB-Engines
NameFaircom EDGE infoformerly c-treeEDGE  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonIngres  Xexclude from comparisonMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data Warehouse  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionFairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environmentsHawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.Well established RDBMSElastic, large scale data warehouse service leveraging the broad eco-system of SQL ServerOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelKey-value store
Relational DBMS
Time Series DBMSRelational DBMSRelational DBMSRelational DBMS
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
Score0.08
Rank#366  Overall
#39  Time Series DBMS
Score3.80
Rank#82  Overall
#44  Relational DBMS
Score19.93
Rank#31  Overall
#19  Relational DBMS
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websitewww.faircom.com/­products/­faircom-edgewww.hawkular.orgwww.actian.com/­databases/­ingresazure.microsoft.com/­services/­synapse-analyticssplicemachine.com
Technical documentationdocs.faircom.com/­docs/­en/­UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.htmlwww.hawkular.org/­hawkular-metrics/­docs/­user-guidedocs.actian.com/­ingresdocs.microsoft.com/­azure/­synapse-analyticssplicemachine.com/­how-it-works
DeveloperFairCom CorporationCommunity supported by Red HatActian CorporationMicrosoftSplice Machine
Initial release197920141974 infooriginally developed at University Berkely in early 1970s20162014
Current releaseV3, October 202011.2, May 20223.1, March 2021
License infoCommercial or Open Sourcecommercial infoRestricted, free version availableOpen Source infoApache 2.0commercialcommercialOpen Source infoAGPL 3.0, commercial license available
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++JavaCC++Java
Server operating systemsAndroid
Linux infoARM, x86
Raspbian
Windows
Linux
OS X
Windows
AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows
hostedLinux
OS X
Solaris
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeyesyesyes
Typing infopredefined data types such as float or dateyes, ANSI Standard SQL Typesyesyesyesyes
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.yesnono infobut tools for importing/exporting data from/to XML-files availableno
Secondary indexesyesnoyesyesyes
SQL infoSupport of SQLyes infoANSI SQL queriesnoyesyesyes
APIs and other access methodsADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API
HTTP REST.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)
ADO.NET
JDBC
ODBC
JDBC
Native Spark Datasource
ODBC
Supported programming languagesC
C#
C++
Java
JavaScript
PHP
Python
VB.Net
Go
Java
Python
Ruby
C#
Java
PHP
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresyes info.Net, JavaScript, C/C++noyesTransact SQLyes infoJava
Triggersyesyes infovia Hawkular Alertingyesnoyes
Partitioning methods infoMethods for storing different data on different nodesFile partitioning infoCustomizable business rules for partitioningSharding infobased on Cassandrahorizontal partitioning infoIngres Star to access multiple databases simultaneouslySharding, horizontal partitioningShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoSynchronous and asynchronous realtime replication based on transaction logsselectable replication factor infobased on CassandraIngres ReplicatoryesMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonononoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency
Tunable Consistency
Eventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyes infowhen using SQLnoyesno infodocs.microsoft.com/­en-us/­azure/­synapse-analytics/­sql-data-warehouse/­sql-data-warehouse-table-constraintsyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayes infoacross SQL and NoSQLyesyes infoMVCCyesyes, multi-version concurrency control (MVCC)
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.yesnonoyes
User concepts infoAccess controlFine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.nofine grained access rights according to SQL-standardyesAccess rights for users, groups and roles according to SQL-standard

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More resources
Faircom EDGE infoformerly c-treeEDGEHawkular MetricsIngresMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data WarehouseSplice Machine
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