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DBMS > Coveo vs. FatDB vs. Hive vs. Spark SQL

System Properties Comparison Coveo vs. FatDB vs. Hive vs. Spark SQL

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Editorial information provided by DB-Engines
NameCoveo  Xexclude from comparisonFatDB  Xexclude from comparisonHive  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.
DescriptionAI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code developmentA .NET NoSQL DBMS that can integrate with and extend SQL Server.data warehouse software for querying and managing large distributed datasets, built on HadoopSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelSearch engineDocument store
Key-value store
Relational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.11
Rank#118  Overall
#11  Search engines
Score59.76
Rank#18  Overall
#12  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitewww.coveo.comhive.apache.orgspark.apache.org/­sql
Technical documentationdocs.coveo.comcwiki.apache.org/­confluence/­display/­Hive/­Homespark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperCoveoFatCloudApache Software Foundation infoinitially developed by FacebookApache Software Foundation
Initial release2012201220122014
Current release3.1.3, April 20223.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache Version 2Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC#JavaScala
Server operating systemshostedWindowsAll OS with a Java VMLinux
OS X
Windows
Data schemehybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configurationschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesyesyes
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.nono
Secondary indexesyesyesyesno
SQL infoSupport of SQLnono infoVia inetgration in SQL ServerSQL-like DML and DDL statementsSQL-like DML and DDL statements
APIs and other access methodsRESTful HTTP API.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
JDBC
ODBC
Thrift
JDBC
ODBC
Supported programming languagesC#
Java
JavaScript
Python
C#C++
Java
PHP
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresnoyes infovia applicationsyes infouser defined functions and integration of map-reduceno
Triggersyesyes infovia applicationsnono
Partitioning methods infoMethods for storing different data on different nodesyesShardingShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyesselectable replication factorselectable replication factornone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyes infoquery execution via MapReduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Eventual Consistency
Foreign keys infoReferential integrityyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesnonono
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.no
User concepts infoAccess controlgranular access controls, API key management, content filtersno infoCan implement custom security layer via applicationsAccess rights for users, groups and rolesno

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CoveoFatDBHiveSpark SQL
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