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DBMS > Apache Impala vs. Kdb vs. Netezza vs. TimescaleDB vs. Yaacomo

System Properties Comparison Apache Impala vs. Kdb vs. Netezza vs. TimescaleDB vs. Yaacomo

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonKdb  Xexclude from comparisonNetezza infoAlso called PureData System for Analytics by IBM  Xexclude from comparisonTimescaleDB  Xexclude from comparisonYaacomo  Xexclude from comparison
Yaacomo seems to be discontinued and is removed from the DB-Engines ranking
DescriptionAnalytic DBMS for HadoopHigh performance Time Series DBMSData warehouse and analytics appliance part of IBM PureSystemsA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQLOpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing
Primary database modelRelational DBMSTime Series DBMS
Vector DBMS
Relational DBMSTime Series DBMSRelational DBMS
Secondary database modelsDocument storeRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score7.55
Rank#53  Overall
#3  Time Series DBMS
#1  Vector DBMS
Score9.06
Rank#46  Overall
#29  Relational DBMS
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Websiteimpala.apache.orgkx.comwww.ibm.com/­products/­netezzawww.timescale.comyaacomo.com
Technical documentationimpala.apache.org/­impala-docs.htmlcode.kx.comdocs.timescale.com
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaKx Systems, a division of First Derivatives plcIBMTimescaleQ2WEB GmbH
Initial release20132000 infokdb was released 2000, kdb+ in 2003200020172009
Current release4.1.0, June 20223.6, May 20182.15.0, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2commercial infofree 32-bit versioncommercialOpen Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++qC
Server operating systemsLinuxLinux
OS X
Solaris
Windows
Linux infoincluded in applianceLinux
OS X
Windows
Android
Linux
Windows
Data schemeyesyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data typesyes
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 indexesyesyes infotable attribute 'grouped'yesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query language (q)yesyes infofull PostgreSQL SQL syntaxyes
APIs and other access methodsJDBC
ODBC
HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket
JDBC
ODBC
OLE DB
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
Supported programming languagesAll languages supporting JDBC/ODBCC
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala
C
C++
Fortran
Java
Lua
Perl
Python
R
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functionsyesuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersnoyes infowith viewsnoyesyes
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioningShardingyes, across time and space (hash partitioning) attributeshorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replicationSource-replica replicationSource-replica replication with hot standby and reads on replicas infoSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceno infosimilar paradigm used for internal processingyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesnoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesnoyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosrights management via user accountsUsers with fine-grained authorization conceptfine grained access rights according to SQL-standardfine grained access rights according to SQL-standard
More information provided by the system vendor
Apache ImpalaKdbNetezza infoAlso called PureData System for Analytics by IBMTimescaleDBYaacomo
Specific characteristicsIntegrated columnar database & programming system for streaming, real time and historical...
» more
Competitive advantagesprovides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more
Typical application scenariostick database streaming sensor data massive intelligence applications oil and gas...
» more
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...
» more
Licensing and pricing modelsupon request
» more

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More resources
Apache ImpalaKdbNetezza infoAlso called PureData System for Analytics by IBMTimescaleDBYaacomo
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