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DBMS > Apache Phoenix vs. Google BigQuery vs. Google Cloud Bigtable vs. Milvus

System Properties Comparison Apache Phoenix vs. Google BigQuery vs. Google Cloud Bigtable vs. Milvus

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonGoogle BigQuery  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonMilvus  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseLarge scale data warehouse service with append-only tablesGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.A DBMS designed for efficient storage of vector data and vector similarity searches
Primary database modelRelational DBMSRelational DBMSKey-value store
Wide column store
Search engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.16
Rank#103  Overall
#52  Relational DBMS
Score50.12
Rank#24  Overall
#16  Relational DBMS
Score5.03
Rank#79  Overall
#12  Key-value stores
#6  Wide column stores
Score0.46
Rank#250  Overall
#17  Search engines
Websitephoenix.apache.orgcloud.google.com/­bigquerycloud.google.com/­bigtablemilvus.io
Technical documentationphoenix.apache.orgcloud.google.com/­bigquery/­docscloud.google.com/­bigtable/­docsmilvus.io/­docs/­overview.md
DeveloperApache Software FoundationGoogleGoogle
Initial release2014201020152019
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20192.0, January 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenoyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++, Go
Server operating systemsLinux
Unix
Windows
hostedhostedLinux
macOS info10.14 or later
Windows infowith WSL 2 enabled
Data schemeyes infolate-bound, schema-on-read capabilitiesyesschema-free
Typing infopredefined data types such as float or dateyesyesnoNumeric and String
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.nononono
Secondary indexesyesnonono
SQL infoSupport of SQLyesyesnono
APIs and other access methodsJDBCRESTful HTTP/JSON APIgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby
C#
C++
Go
Java
JavaScript (Node.js)
Python
C++
Go
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresuser defined functionsuser defined functions infoin JavaScriptnono
Triggersnononono
Partitioning methods infoMethods for storing different data on different nodesShardingnoneShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Internal replication in Colossus, and regional replication between two clusters in different zones
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationnoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDno infoSince BigQuery is designed for querying dataAtomic single-row operationsno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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.yesnonono
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyAccess privileges (owner, writer, reader) on dataset, table or view level infoGoogle Cloud Identity & Access Management (IAM)Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)no
More information provided by the system vendor
Apache PhoenixGoogle BigQueryGoogle Cloud BigtableMilvus
Specific characteristicsMilvus is an open-source and cloud-native vector database built for production-ready...
» more
Competitive advantagesHighly available, versatile, and robust with millisecond latency. Supports batch...
» more
Typical application scenariosVideo media : video understanding, video deduplication. E-commerce and mobile applications...
» more
Key customersMilvus is trusted by over 1,000 enterprises, including Baidu, eBay, IKEA, LINE, Shopee,...
» more
Market metricsAs of March 2022, 9.8k+ GitHub stars 1.4k+ Github forks 871k+ docker pulls 2k+ community...
» more
Licensing and pricing modelsMilvus was released under the open-source Apache License 2.0 in October 2019. Managed...
» more

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More resources
Apache PhoenixGoogle BigQueryGoogle Cloud BigtableMilvus
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