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DBMS > Hive vs. SWC-DB vs. Vertica

System Properties Comparison Hive vs. SWC-DB vs. Vertica

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Editorial information provided by DB-Engines
NameHive  Xexclude from comparisonSWC-DB infoSuper Wide Column Database  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
Descriptiondata warehouse software for querying and managing large distributed datasets, built on HadoopA high performance, scalable Wide Column DBMSCloud 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.
Primary database modelRelational DBMSWide column storeRelational DBMS infoColumn oriented
Secondary database modelsTime Series DBMSSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score61.17
Rank#18  Overall
#12  Relational DBMS
Score0.01
Rank#376  Overall
#13  Wide column stores
Score10.68
Rank#43  Overall
#27  Relational DBMS
Websitehive.apache.orggithub.com/­kashirin-alex/­swc-db
www.swcdb.org
www.vertica.com
Technical documentationcwiki.apache.org/­confluence/­display/­Hive/­Homevertica.com/­documentation
DeveloperApache Software Foundation infoinitially developed by FacebookAlex KashirinOpenText infopreviously Micro Focus and Hewlett Packard
Initial release201220202005
Current release3.1.3, April 20220.5, April 202112.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoGPL V3commercial 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

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Implementation languageJavaC++C++
Server operating systemsAll OS with a Java VMLinuxLinux
Data schemeyesschema-freeYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.
Typing infopredefined data types such as float or dateyesyes
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.nono
Secondary indexesyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query languageFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsJDBC
ODBC
Thrift
Proprietary protocol
Thrift
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesC++
Java
PHP
Python
C++C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenoyes, PostgreSQL PL/pgSQL, with minor differences
Triggersnonoyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesShardingShardinghorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorMulti-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 methodsyes infoquery execution via MapReducenono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID
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.nono
User concepts infoAccess controlAccess rights for users, groups and rolesfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
HiveSWC-DB infoSuper Wide Column DatabaseVertica infoOpenText™ Vertica™
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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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,...
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Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
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More resources
HiveSWC-DB infoSuper Wide Column DatabaseVertica infoOpenText™ Vertica™
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