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DBMS > FatDB vs. HBase vs. Spark SQL vs. TimescaleDB

System Properties Comparison FatDB vs. HBase vs. Spark SQL vs. TimescaleDB

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Editorial information provided by DB-Engines
NameFatDB  Xexclude from comparisonHBase  Xexclude from comparisonSpark SQL  Xexclude from comparisonTimescaleDB  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
DescriptionA .NET NoSQL DBMS that can integrate with and extend SQL Server.Wide-column store based on Apache Hadoop and on concepts of BigTableSpark SQL is a component on top of 'Spark Core' for structured data processingA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelDocument store
Key-value store
Wide column storeRelational DBMSTime Series DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score30.50
Rank#26  Overall
#2  Wide column stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Websitehbase.apache.orgspark.apache.org/­sqlwww.timescale.com
Technical documentationhbase.apache.org/­book.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.timescale.com
DeveloperFatCloudApache Software Foundation infoApache top-level project, originally developed by PowersetApache Software FoundationTimescale
Initial release2012200820142017
Current release2.3.4, January 20213.5.0 ( 2.13), September 20232.13.0, November 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache version 2Open Source infoApache 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC#JavaScalaC
Server operating systemsWindowsLinux
Unix
Windows infousing Cygwin
Linux
OS X
Windows
Linux
OS X
Windows
Data schemeschema-freeschema-free, schema definition possibleyesyes
Typing infopredefined data types such as float or dateyesoptions to bring your own types, AVROyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex 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.nonoyes
Secondary indexesyesnonoyes
SQL infoSupport of SQLno infoVia inetgration in SQL ServernoSQL-like DML and DDL statementsyes infofull PostgreSQL SQL syntax
APIs and other access methods.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
Java API
RESTful HTTP API
Thrift
JDBC
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesC#C
C#
C++
Groovy
Java
PHP
Python
Scala
Java
Python
R
Scala
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresyes infovia applicationsyes infoCoprocessors in Javanouser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersyes infovia applicationsyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes, utilizing Spark Coreyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorMulti-source replication
Source-replica replication
noneSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate Consistency or Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoSingle row ACID (across millions of columns)noACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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.yesnono
User concepts infoAccess controlno infoCan implement custom security layer via applicationsAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACnofine grained access rights according to SQL-standard

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More resources
FatDBHBaseSpark SQLTimescaleDB
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