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DBMS > EJDB vs. HBase vs. Vertica

System Properties Comparison EJDB vs. HBase vs. Vertica

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Editorial information provided by DB-Engines
NameEJDB  Xexclude from comparisonHBase  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionEmbeddable document-store database library with JSON representation of queries (in MongoDB style)Wide-column store based on Apache Hadoop and on concepts of BigTableCloud 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 modelDocument storeWide column storeRelational DBMS infoColumn oriented
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.30
Rank#294  Overall
#44  Document stores
Score31.25
Rank#26  Overall
#2  Wide column stores
Score11.40
Rank#43  Overall
#27  Relational DBMS
Websitegithub.com/­Softmotions/­ejdbhbase.apache.orgwww.vertica.com
Technical documentationgithub.com/­Softmotions/­ejdb/­blob/­master/­README.mdhbase.apache.org/­book.htmlvertica.com/­documentation
DeveloperSoftmotionsApache Software Foundation infoApache top-level project, originally developed by PowersetOpenText infopreviously Micro Focus and Hewlett Packard
Initial release201220082005
Current release2.3.4, January 202112.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoGPLv2Open Source infoApache version 2commercial 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 languageCJavaC++
Server operating systemsserver-lessLinux
Unix
Windows infousing Cygwin
Linux
Data schemeschema-freeschema-free, schema definition possibleYes, 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 dateyes infostring, integer, double, bool, date, object_idoptions to bring your own types, AVROyes
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 indexesnonoNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLnonoFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsin-process shared libraryJava API
RESTful HTTP API
Thrift
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesActionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby
C
C#
C++
Groovy
Java
PHP
Python
Scala
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresnoyes infoCoprocessors in Javayes, PostgreSQL PL/pgSQL, with minor differences
Triggersnoyesyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesnoneShardinghorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesnoneMulti-source replication
Source-replica replication
Multi-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 methodsnoyesno infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integrityno infotypically not needed, however similar functionality with collection joins possiblenoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoSingle row ACID (across millions of columns)ACID
Concurrency infoSupport for concurrent manipulation of datayes infoRead/Write Lockingyesyes
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.yesno
User concepts infoAccess controlnoAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
EJDBHBaseVertica 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...
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More resources
EJDBHBaseVertica infoOpenText™ Vertica™
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