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DBMS > Apache IoTDB vs. Brytlyt vs. Fujitsu Enterprise Postgres vs. HBase

System Properties Comparison Apache IoTDB vs. Brytlyt vs. Fujitsu Enterprise Postgres vs. HBase

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonBrytlyt  Xexclude from comparisonFujitsu Enterprise Postgres  Xexclude from comparisonHBase  Xexclude from comparison
DescriptionAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkScalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQLEnterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.Wide-column store based on Apache Hadoop and on concepts of BigTable
Primary database modelTime Series DBMSRelational DBMSRelational DBMSWide column store
Secondary database modelsDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score0.29
Rank#288  Overall
#131  Relational DBMS
Score0.31
Rank#285  Overall
#129  Relational DBMS
Score30.50
Rank#26  Overall
#2  Wide column stores
Websiteiotdb.apache.orgbrytlyt.iowww.postgresql.fastware.comhbase.apache.org
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.brytlyt.iowww.postgresql.fastware.com/­product-manualshbase.apache.org/­book.html
DeveloperApache Software FoundationBrytlytPostgreSQL Global Development Group, Fujitsu Australia Software TechnologyApache Software Foundation infoApache top-level project, originally developed by Powerset
Initial release201820162008
Current release1.1.0, April 20235.0, August 2023Fujitsu Enterprise Postgres 14, January 20222.3.4, January 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialOpen Source infoApache version 2
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC, C++ and CUDACJava
Server operating systemsAll OS with a Java VM (>= 1.8)Linux
OS X
Windows
Linux
Windows
Linux
Unix
Windows infousing Cygwin
Data schemeyesyesyesschema-free, schema definition possible
Typing infopredefined data types such as float or dateyesyesyesoptions to bring your own types, AVRO
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.noyes infospecific XML-type available, but no XML query functionality.no
Secondary indexesyesyesyesno
SQL infoSupport of SQLSQL-like query languageyesyesno
APIs and other access methodsJDBC
Native API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Java API
RESTful HTTP API
Thrift
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
.Net
C
C++
Delphi
Java
Perl
Python
Tcl
.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C
C#
C++
Groovy
Java
PHP
Python
Scala
Server-side scripts infoStored proceduresyesuser defined functions infoin PL/pgSQLuser defined functionsyes infoCoprocessors in Java
Triggersyesyesyesyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)partitioning by range, list and by hashSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasSource-replica replicationSource-replica replicationMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparknonoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency
Foreign keys infoReferential integritynoyesyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDSingle row ACID (across millions of columns)
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)yes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes
User concepts infoAccess controlyesfine grained access rights according to SQL-standardfine grained access rights according to SQL-standardAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC
More information provided by the system vendor
Apache IoTDBBrytlytFujitsu Enterprise PostgresHBase
Specific characteristics100% compatible with community PostgreSQL
» more
Competitive advantagesBuilt-in TDE and Data Masking security. In-Memory Columnar Index, and a high speed...
» more
Typical application scenariosTransactional payments applications, reporting and mixed workloads.
» more
Market metricsOver 30 years experience in database technology. Over 20 years in Postgres development...
» more
Licensing and pricing modelsCore based licensing
» more

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Apache IoTDBBrytlytFujitsu Enterprise PostgresHBase
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