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DBMS > Apache Pinot vs. FatDB vs. InterSystems Caché vs. Oracle Berkeley DB vs. Spark SQL

System Properties Comparison Apache Pinot vs. FatDB vs. InterSystems Caché vs. Oracle Berkeley DB vs. Spark SQL

Editorial information provided by DB-Engines
NameApache Pinot  Xexclude from comparisonFatDB  Xexclude from comparisonInterSystems Caché  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparisonSpark SQL  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.
DescriptionRealtime distributed OLAP datastore, designed to answer OLAP queries with low latencyA .NET NoSQL DBMS that can integrate with and extend SQL Server.A multi-model DBMS and application serverWidely used in-process key-value storeSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSDocument store
Key-value store
Key-value store
Object oriented DBMS
Relational DBMS
Key-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
Relational DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.38
Rank#275  Overall
#126  Relational DBMS
Score2.01
Rank#126  Overall
#21  Key-value stores
#3  Native XML DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitepinot.apache.orgwww.intersystems.com/­products/­cachewww.oracle.com/­database/­technologies/­related/­berkeleydb.htmlspark.apache.org/­sql
Technical documentationdocs.pinot.apache.orgdocs.intersystems.comdocs.oracle.com/­cd/­E17076_05/­html/­index.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software Foundation and contributorsFatCloudInterSystemsOracle infooriginally developed by Sleepycat, which was acquired by OracleApache Software Foundation
Initial release20152012199719942014
Current release1.0.0, September 20232018.1.4, May 202018.1.40, May 20203.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialOpen Source infocommercial license availableOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC#C, Java, C++ (depending on the Berkeley DB edition)Scala
Server operating systemsAll OS with a Java JDK11 or higherWindowsAIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows
AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
Linux
OS X
Windows
Data schemeyesschema-freedepending on used data modelschema-freeyes
Typing infopredefined data types such as float or dateyesyesyesnoyes
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 infoonly with the Berkeley DB XML editionno
Secondary indexesyesyesyesno
SQL infoSupport of SQLSQL-like query languageno infoVia inetgration in SQL Serveryesyes infoSQL interfaced based on SQLite is availableSQL-like DML and DDL statements
APIs and other access methodsJDBC.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
.NET Client API
JDBC
ODBC
RESTful HTTP API
JDBC
ODBC
Supported programming languagesGo
Java
Python
C#C#
C++
Java
.Net infoFigaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others infoThird-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js) info3rd party binding
Perl
Python
Tcl
Java
Python
R
Scala
Server-side scripts infoStored proceduresyes infovia applicationsyesnono
Triggersyes infovia applicationsyesyes infoonly for the SQL APIno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningShardingnonenoneyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replicationSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
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.yesyesno
User concepts infoAccess controlno infoCan implement custom security layer via applicationsAccess rights for users, groups and rolesnono

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More resources
Apache PinotFatDBInterSystems CachéOracle Berkeley DBSpark SQL
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