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DBMS > EXASOL vs. Milvus vs. Vertica

System Properties Comparison EXASOL vs. Milvus vs. Vertica

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Editorial information provided by DB-Engines
NameEXASOL  Xexclude from comparisonMilvus  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionHigh-performance, in-memory, MPP database specifically designed for in-memory analytics.A DBMS designed for efficient storage of vector data and vector similarity searchesCloud 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 DBMSVector DBMSRelational DBMS infoColumn oriented
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.99
Rank#124  Overall
#58  Relational DBMS
Score2.31
Rank#113  Overall
#3  Vector DBMS
Score10.68
Rank#43  Overall
#27  Relational DBMS
Websitewww.exasol.commilvus.iowww.vertica.com
Technical documentationwww.exasol.com/­resourcesmilvus.io/­docs/­overview.mdvertica.com/­documentation
DeveloperExasolOpenText infopreviously Micro Focus and Hewlett Packard
Initial release200020192005
Current release2.3.4, January 202412.0.3, January 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0commercial 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

Providers of DBaaS offerings, please contact us to be listed.
Zilliz Cloud – Cloud-native service for Milvus
Implementation languageC++, GoC++
Server operating systemsLinux
macOS info10.14 or later
Windows infowith WSL 2 enabled
Linux
Data schemeyesYes, 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 dateyesVector, Numeric and Stringyes
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 indexesyesnoNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLyesnoFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methods.Net
JDBC
ODBC
WebSocket
RESTful HTTP APIADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesJava
Lua
Python
R
C++
Go
Java
JavaScript (Node.js)
Python
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresuser defined functionsnoyes, PostgreSQL PL/pgSQL, with minor differences
Triggersyesnoyes, 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 nodesMulti-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 infoHadoop integrationnono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyBounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency
Immediate Consistency
Foreign keys infoReferential integrityyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACID
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.yesyesno
User concepts infoAccess controlAccess rights for users, groups and roles according to SQL-standardRole based access control and fine grained access rightsfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
EXASOLMilvusVertica infoOpenText™ Vertica™
Specific characteristicsMilvus is an open-source and cloud-native vector database built for production-ready...
» more
Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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Competitive advantagesHighly available, versatile, and robust with millisecond latency. Supports batch...
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Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenariosRAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more
Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customersMilvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more
Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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Market metricsAs of January 2024, 25k+ GitHub stars 10M+ downloads and installations​ ​ 3k+ enterprise...
» more
Licensing and pricing modelsMilvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
» more
Cost-based models and subscription-based models are both available. One license is...
» more

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More resources
EXASOLMilvusVertica infoOpenText™ Vertica™
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