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DBMS > Apache Impala vs. Datastax Enterprise vs. TimescaleDB vs. Valentina Server

System Properties Comparison Apache Impala vs. Datastax Enterprise vs. TimescaleDB vs. Valentina Server

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonDatastax Enterprise  Xexclude from comparisonTimescaleDB  Xexclude from comparisonValentina Server  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopDataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQLObject-relational database and reports server
Primary database modelRelational DBMSWide column storeTime Series DBMSRelational DBMS
Secondary database modelsDocument storeDocument store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score5.80
Rank#60  Overall
#4  Wide column stores
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Score0.17
Rank#327  Overall
#145  Relational DBMS
Websiteimpala.apache.orgwww.datastax.com/­products/­datastax-enterprisewww.timescale.comwww.valentina-db.net
Technical documentationimpala.apache.org/­impala-docs.htmldocs.datastax.comdocs.timescale.comvalentina-db.com/­docs/­dokuwiki/­v5/­doku.php
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaDataStaxTimescaleParadigma Software
Initial release2013201120171999
Current release4.1.0, June 20226.8, April 20202.15.0, May 20245.7.5
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialOpen Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++JavaC
Server operating systemsLinuxLinux
OS X
Linux
OS X
Windows
Linux
OS X
Windows
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesnumerics, 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.nonoyes
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like DML and DDL statements (CQL); Spark SQLyes infofull PostgreSQL SQL syntaxyes
APIs and other access methodsJDBC
ODBC
Proprietary protocol infoCQL (Cassandra Query Language)
TinkerPop Gremlin infowith DSE Graph
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
ODBC
Supported programming languagesAll languages supporting JDBC/ODBCC
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
.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-reducenouser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shellyes
Triggersnoyesyesyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infono "single point of failure"yes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorconfigurable replication factor, datacenter aware, advanced replication for edge computingSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency
Tunable Consistency infoconsistency level can be individually decided with each write operation
Immediate Consistency
Foreign keys infoReferential integritynonoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono infoAtomicity and isolation are supported for single operationsACID
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.noyesnoyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosAccess rights for users can be defined per objectfine grained access rights according to SQL-standardfine grained access rights according to SQL-standard
More information provided by the system vendor
Apache ImpalaDatastax EnterpriseTimescaleDBValentina Server
Specific characteristicsDataStax Enterprise is scale-out data infrastructure for enterprises that need to...
» more
Competitive advantagesSupporting the following application requirements: Zero downtime - Built on Apache...
» more
Typical application scenariosApplications that must be massively and linearly scalable with 100% uptime and able...
» more
Key customersCapital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
» more
Market metricsAmong the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
» more
Licensing and pricing modelsAnnual subscription
» more

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Apache ImpalaDatastax EnterpriseTimescaleDBValentina Server
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