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DBMS > Databend vs. Realm vs. STSdb vs. Vertica

System Properties Comparison Databend vs. Realm vs. STSdb vs. Vertica

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Editorial information provided by DB-Engines
NameDatabend  Xexclude from comparisonRealm  Xexclude from comparisonSTSdb  Xexclude from comparisonVertica infoOpenText™ Vertica™  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 complexityA DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core DataKey-Value Store with special method for indexing infooptimized for high performance using a special indexing methodCloud 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 DBMSDocument storeKey-value storeRelational DBMS infoColumn oriented
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.28
Rank#287  Overall
#131  Relational DBMS
Score7.18
Rank#52  Overall
#8  Document stores
Score0.03
Rank#365  Overall
#54  Key-value stores
Score9.62
Rank#42  Overall
#26  Relational DBMS
Websitegithub.com/­datafuselabs/­databend
www.databend.com
realm.iogithub.com/­STSSoft/­STSdb4www.vertica.com
Technical documentationdocs.databend.comrealm.io/­docsvertica.com/­documentation
DeveloperDatabend LabsRealm, acquired by MongoDB in May 2019STS Soft SCOpenText infopreviously Micro Focus and Hewlett Packard
Initial release2021201420112005
Current release1.0.59, April 20234.0.8, September 201512.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open SourceOpen Source infoGPLv2, commercial license availablecommercial 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 languageRustC#C++
Server operating systemshosted
Linux
macOS
Android
Backend: server-less
iOS
Windows
WindowsLinux
Data schemeyesyesyesYes, 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 dateyesyesyes infoprimitive types and user defined types (classes)yes
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 indexesnoyesnoNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLyesnonoFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsCLI Client
JDBC
RESTful HTTP API
.NET Client APIADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesGo
Java
JavaScript (Node.js)
Python
Rust
.Net
Java infowith Android only
Objective-C
React Native
Swift
C#
Java
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresnono inforuns within the applications so server-side scripts are unnecessarynoyes, PostgreSQL PL/pgSQL, with minor differences
Triggersnoyes infoChange Listenersnoyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesnonenonenonehorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesnonenonenoneMulti-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 methodsnononono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes infoIn-Memory realmno
User concepts infoAccess controlUsers with fine-grained authorization concept, user rolesyesnofine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

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More resources
DatabendRealmSTSdbVertica infoOpenText™ Vertica™
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