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DBMS > Apache Impala vs. Databricks vs. Kdb vs. PouchDB vs. Valentina Server

System Properties Comparison Apache Impala vs. Databricks vs. Kdb vs. PouchDB vs. Valentina Server

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonDatabricks  Xexclude from comparisonKdb  Xexclude from comparisonPouchDB  Xexclude from comparisonValentina Server  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopThe Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.High performance Time Series DBMSJavaScript DBMS with an API inspired by CouchDBObject-relational database and reports server
Primary database modelRelational DBMSDocument store
Relational DBMS
Time Series DBMS
Vector DBMS
Document storeRelational DBMS
Secondary database modelsDocument storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score12.45
Rank#40  Overall
#24  Relational DBMS
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score7.71
Rank#49  Overall
#2  Time Series DBMS
#1  Vector DBMS
Score2.34
Rank#112  Overall
#21  Document stores
Score0.21
Rank#325  Overall
#144  Relational DBMS
Websiteimpala.apache.orgwww.databricks.comkx.compouchdb.comwww.valentina-db.net
Technical documentationimpala.apache.org/­impala-docs.htmldocs.databricks.comcode.kx.compouchdb.com/­guidesvalentina-db.com/­docs/­dokuwiki/­v5/­doku.php
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaDatabricksKx Systems, a division of First Derivatives plcApache Software FoundationParadigma Software
Initial release201320132000 infokdb was released 2000, kdb+ in 200320121999
Current release4.1.0, June 20223.6, May 20187.1.1, June 20195.7.5
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercial infofree 32-bit versionOpen Sourcecommercial
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++qJavaScript
Server operating systemsLinuxhostedLinux
OS X
Solaris
Windows
server-less, requires a JavaScript environment (browser, Node.js)Linux
OS X
Windows
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)yesschema-freeyes
Typing infopredefined data types such as float or dateyesyesnoyes
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.noyesyesno
Secondary indexesyesyesyes infotable attribute 'grouped'yes infovia viewsyes
SQL infoSupport of SQLSQL-like DML and DDL statementswith Databricks SQLSQL-like query language (q)noyes
APIs and other access methodsJDBC
ODBC
JDBC
ODBC
RESTful HTTP API
HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket
HTTP REST infoonly for PouchDB Server
JavaScript API
ODBC
Supported programming languagesAll languages supporting JDBC/ODBCPython
R
Scala
C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala
JavaScript.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functions and aggregatesuser defined functionsView functions in JavaScriptyes
Triggersnoyes infowith viewsyesyes
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioningSharding infowith a proxy-based framework, named couchdb-lounge
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryesSource-replica replicationMulti-source replication infoalso with CouchDB databases
Source-replica replication infoalso with CouchDB databases
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceno infosimilar paradigm used for internal processingyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoby using IndexedDB, WebSQL or LevelDB as backendyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesyesyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosrights management via user accountsnofine grained access rights according to SQL-standard
More information provided by the system vendor
Apache ImpalaDatabricksKdbPouchDBValentina Server
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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Integrated columnar database & programming system for streaming, real time and historical...
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Competitive advantagesprovides seamless scalability; runs on industry standard server platforms; is top-ranked...
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Typical application scenariostick database streaming sensor data massive intelligence applications oil and gas...
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Key customersGoldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more
Market metricskdb+ performance and reliability proven by our customers in critical infrastructure...
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Licensing and pricing modelsupon request
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More resources
Apache ImpalaDatabricksKdbPouchDBValentina Server
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