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DBMS > Cachelot.io vs. EsgynDB vs. Hive vs. Vertica

System Properties Comparison Cachelot.io vs. EsgynDB vs. Hive vs. Vertica

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Editorial information provided by DB-Engines
NameCachelot.io  Xexclude from comparisonEsgynDB  Xexclude from comparisonHive  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionIn-memory caching systemEnterprise-class SQL-on-Hadoop solution, powered by Apache Trafodiondata warehouse software for querying and managing large distributed datasets, built on HadoopCloud 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 modelKey-value storeRelational DBMSRelational DBMSRelational DBMS infoColumn oriented
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.04
Rank#388  Overall
#62  Key-value stores
Score0.25
Rank#312  Overall
#138  Relational DBMS
Score59.76
Rank#18  Overall
#12  Relational DBMS
Score10.06
Rank#42  Overall
#26  Relational DBMS
Websitecachelot.iowww.esgyn.cnhive.apache.orgwww.vertica.com
Technical documentationcwiki.apache.org/­confluence/­display/­Hive/­Homevertica.com/­documentation
DeveloperEsgynApache Software Foundation infoinitially developed by FacebookOpenText infopreviously Micro Focus and Hewlett Packard
Initial release2015201520122005
Current release3.1.3, April 202212.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoSimplified BSD LicensecommercialOpen Source infoApache Version 2commercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenononono 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 languageC++C++, JavaJavaC++
Server operating systemsFreeBSD
Linux
OS X
LinuxAll OS with a Java VMLinux
Data schemeschema-freeyesyesYes, 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 datenoyesyesyes
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 indexesnoyesyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLnoyesSQL-like DML and DDL statementsFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsMemcached protocolADO.NET
JDBC
ODBC
JDBC
ODBC
Thrift
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languages.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby
All languages supporting JDBC/ODBC/ADO.NetC++
Java
PHP
Python
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresnoJava Stored Proceduresyes infouser defined functions and integration of map-reduceyes, PostgreSQL PL/pgSQL, with minor differences
Triggersnononoyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardinghorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesnoneMulti-source replication between multi datacentersselectable 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 methodsnoyesyes infoquery execution via MapReduceno infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentnoyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlnofine grained access rights according to SQL-standardAccess 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
Cachelot.ioEsgynDBHiveVertica 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,...
» more
Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
» more

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More resources
Cachelot.ioEsgynDBHiveVertica infoOpenText™ Vertica™
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