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DBMS > Postgres-XL vs. Transbase vs. Vertica

System Properties Comparison Postgres-XL vs. Transbase vs. Vertica

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Editorial information provided by DB-Engines
NamePostgres-XL  Xexclude from comparisonTransbase  Xexclude from comparisonVertica infoVertica Analytics Platform  Xexclude from comparison
DescriptionBased on PostgreSQL enhanced with MPP and write-scale-out cluster featuresA resource-optimized, high-performance, universally applicable RDBMSCloud or on-premises analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.
Primary database modelRelational DBMSRelational DBMSRelational DBMS infoColumn oriented
Secondary database modelsDocument store
Spatial DBMS
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.71
Rank#208  Overall
#99  Relational DBMS
Score0.08
Rank#324  Overall
#146  Relational DBMS
Score19.04
Rank#38  Overall
#24  Relational DBMS
Websitewww.postgres-xl.orgwww.transaction.de/­en/­products/­transbase.htmlwww.vertica.com
Technical documentationwww.postgres-xl.org/­documentationwww.transaction.de/­en/­products/­transbase/­features.htmlvertica.com/­documentation
DeveloperTransaction Software GmbHMicro Focus infoprior to that Hewlett Packard
Initial release2014 infosince 2012, originally named StormDB19872005
Current release10 R1, October 2018Transbase 8.4, September 202011.1, February 2022
License infoCommercial or Open SourceOpen Source infoMozilla public licensecommercial infofree development licensecommercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenonono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Vertica Accelerator’s high-performance analytics and machine learning with SQL or Python is available as a managed service.
Implementation languageCC and C++C++
Server operating systemsLinux
macOS
FreeBSD
Linux
macOS
Solaris
Windows
Linux
Data schemeyesyesyes, but unstructured data can be stored in specific Flex-Tables
Typing infopredefined data types such as float or dateyesyesyes: BINARY, BOOLEAN, CHAR, VARCHAR, LONG VARCHAR, DATE, TIME, TIMESTAMP, INTERVAL, INTERVAL DAY TO SECOND, INTERVAL YEAR TO MONTH, DOUBLE PRECISION, FLOAT, INTEGER, BIGINT, SMALLINT,NUMERIC, DECIMAL, NUMBER, MONEY,GEOMETRY
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.yes infoXML type, but no XML query functionalitynono
Secondary indexesyesyesNo Indexes Required
SQL infoSupport of SQLyes infodistributed, parallel query executionyesFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
ADO.NET
JDBC
ODBC
Proprietary native API
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languages.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python
C#
C++
Java
Perl
PHP
Python
R
Server-side scripts infoStored proceduresuser defined functionsyesyes, PostgreSQL PL/pgSQL, with minor differences
Triggersyesyesyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioninghorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoMVCCyesACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlfine grained access rights according to SQL-standardfine grained access rights according to SQL-standardfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
Postgres-XLTransbaseVertica infoVertica Analytics Platform
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy on-premises,...
» more
Competitive advantagesFast, scalable and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more
Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
» more

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More resources
Postgres-XLTransbaseVertica infoVertica Analytics Platform
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