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DBMS > Blueflood vs. Faircom DB vs. GridDB vs. IBM Db2 warehouse vs. Microsoft Azure Data Explorer

System Properties Comparison Blueflood vs. Faircom DB vs. GridDB vs. IBM Db2 warehouse vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines
NameBlueflood  Xexclude from comparisonFaircom DB infoformerly c-treeACE  Xexclude from comparisonGridDB  Xexclude from comparisonIBM Db2 warehouse infoformerly named IBM dashDB  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionScalable TimeSeries DBMS based on CassandraNative high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.Scalable in-memory time series database optimized for IoT and Big DataCloud-based data warehousing serviceFully managed big data interactive analytics platform
Primary database modelTime Series DBMSKey-value store
Relational DBMS
Time Series DBMSRelational DBMSRelational DBMS infocolumn oriented
Secondary database modelsKey-value store
Relational DBMS
Document 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
Score0.13
Rank#346  Overall
#33  Time Series DBMS
Score0.29
Rank#304  Overall
#43  Key-value stores
#136  Relational DBMS
Score2.09
Rank#120  Overall
#10  Time Series DBMS
Score1.37
Rank#160  Overall
#74  Relational DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Websiteblueflood.iowww.faircom.com/­products/­faircom-dbgriddb.netwww.ibm.com/­products/­db2/­warehouseazure.microsoft.com/­services/­data-explorer
Technical documentationgithub.com/­rax-maas/­blueflood/­wikidocs.faircom.com/­docs/­en/­UUID-7446ae34-a1a7-c843-c894-d5322e395184.htmldocs.griddb.netdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperRackspaceFairCom CorporationToshiba CorporationIBMMicrosoft
Initial release20131979201320142019
Current releaseV12, November 20205.1, August 2022cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache 2.0commercial infoRestricted, free version availableOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also 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 languageJavaANSI C, C++C++
Server operating systemsLinux
OS X
AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows infoeasily portable to other OSs
Linuxhostedhosted
Data schemepredefined schemeschema free, schema optional, schema required, partial schema,yesyesFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesyes, ANSI SQL Types, JSON, typed binary structuresyes infonumerical, string, blob, geometry, boolean, timestampyesyes 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.nononono infoImport/export of XML data possibleyes
Secondary indexesnoyesyesyesall fields are automatically indexed
SQL infoSupport of SQLnoyes, ANSI SQL with proprietary extensionsSQL92, SQL-like TQL (Toshiba Query Language)yesKusto Query Language (KQL), SQL subset
APIs and other access methodsHTTP RESTADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
.NET Client API
JDBC
ODBC
OLE DB
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languages.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresnoyes info.Net, JavaScript, C/C++noPL/SQL, SQL PLYes, possible languages: KQL, Python, R
Triggersnoyesyesyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesSharding infobased on CassandraFile partitioning, horizontal partitioning, sharding infoCustomizable business rules for table partitioningShardingShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor infobased on Cassandrayes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).Source-replica replicationyesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsnoSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction
Immediate consistency within container, eventual consistency across containersImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyesnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanotunable from ACID to Eventually ConsistentACID at container levelACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
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.noyesyesyesno
User concepts infoAccess controlnoFine grained access rights according to SQL-standard with additional protections for filesAccess rights for users can be defined per databasefine grained access rights according to SQL-standardAzure Active Directory Authentication
More information provided by the system vendor
BluefloodFaircom DB infoformerly c-treeACEGridDBIBM Db2 warehouse infoformerly named IBM dashDBMicrosoft Azure Data Explorer
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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More resources
BluefloodFaircom DB infoformerly c-treeACEGridDBIBM Db2 warehouse infoformerly named IBM dashDBMicrosoft Azure Data Explorer
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