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DBMS > Databend vs. Vertica vs. Warp 10

System Properties Comparison Databend vs. Vertica vs. Warp 10

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Editorial information provided by DB-Engines
NameDatabend  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparisonWarp 10  Xexclude from comparison
DescriptionAn open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexityCloud or off-cloud 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.TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase
Primary database modelRelational DBMSRelational DBMS infoColumn orientedTime Series DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.34
Rank#283  Overall
#130  Relational DBMS
Score10.06
Rank#42  Overall
#26  Relational DBMS
Score0.14
Rank#344  Overall
#32  Time Series DBMS
Websitegithub.com/­datafuselabs/­databend
www.databend.com
www.vertica.comwww.warp10.io
Technical documentationdocs.databend.comvertica.com/­documentationwww.warp10.io/­content/­02_Getting_started
DeveloperDatabend LabsOpenText infopreviously Micro Focus and Hewlett PackardSenX
Initial release202120052015
Current release1.0.59, April 202312.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercial infoLimited community edition freeOpen Source infoApache License 2.0
Cloud-based only infoOnly available as a cloud servicenono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containersno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageRustC++Java
Server operating systemshosted
Linux
macOS
LinuxLinux
OS X
Windows
Data schemeyesYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.schema-free
Typing infopredefined data types such as float or dateyesyesyes
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.nonono
Secondary indexesnoNo Indexes Required. Different internal optimization strategy, but same functionality included.no
SQL infoSupport of SQLyesFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.no
APIs and other access methodsCLI Client
JDBC
RESTful HTTP API
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
HTTP API
Jupyter
WebSocket
Supported programming languagesGo
Java
JavaScript (Node.js)
Python
Rust
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresnoyes, PostgreSQL PL/pgSQL, with minor differencesyes infoWarpScript
Triggersnoyes, called Custom Alertsno
Partitioning methods infoMethods for storing different data on different nodesnonehorizontal partitioning, hierarchical partitioningSharding infobased on HBase
Replication methods infoMethods for redundantly storing data on multiple nodesnoneMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.selectable replication factor infobased on HBase
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono infoBi-directional Spark integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency infobased on HBase
Foreign keys infoReferential integritynoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesACIDno
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.noyes
User concepts infoAccess controlUsers with fine-grained authorization concept, user rolesfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hashMandatory use of cryptographic tokens, containing fine-grained authorizations
More information provided by the system vendor
DatabendVertica infoOpenText™ Vertica™Warp 10
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» 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...
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DatabendVertica infoOpenText™ Vertica™Warp 10
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