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DBMS > Google BigQuery vs. Google Cloud Datastore vs. PostgreSQL vs. Qdrant

System Properties Comparison Google BigQuery vs. Google Cloud Datastore vs. PostgreSQL vs. Qdrant

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Editorial information provided by DB-Engines
NameGoogle BigQuery  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonPostgreSQL  Xexclude from comparisonQdrant  Xexclude from comparison
DescriptionLarge scale data warehouse service with append-only tablesAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformWidely used open source RDBMS infoDeveloped as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQLA high-performance vector database with neural network or semantic-based matching
Primary database modelRelational DBMSDocument storeRelational DBMS infowith object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.Vector DBMS
Secondary database modelsDocument store
Graph DBMS infowith Apache Age
Spatial DBMS
Vector DBMS infowith pgvector extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score60.38
Rank#19  Overall
#13  Relational DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Score645.54
Rank#4  Overall
#4  Relational DBMS
Score1.16
Rank#175  Overall
#6  Vector DBMS
Websitecloud.google.com/­bigquerycloud.google.com/­datastorewww.postgresql.orggithub.com/­qdrant/­qdrant
qdrant.tech
Technical documentationcloud.google.com/­bigquery/­docscloud.google.com/­datastore/­docswww.postgresql.org/­docsqdrant.tech/­documentation
DeveloperGoogleGooglePostgreSQL Global Development Group infowww.postgresql.org/­developerQdrant
Initial release201020081989 info1989: Postgres, 1996: PostgreSQL2021
Current release16.3, May 2024
License infoCommercial or Open SourcecommercialcommercialOpen Source infoBSDOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud serviceyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCRust
Server operating systemshostedhostedFreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows
Docker
Linux
macOS
Windows
Data schemeyesschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesyes, details hereyesNumbers, Strings, Geo, Boolean
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.nonoyes infospecific XML-type available, but no XML query functionality.no
Secondary indexesnoyesyesyes infoKeywords, numberic ranges, geo, full-text
SQL infoSupport of SQLyesSQL-like query language (GQL)yes infostandard with numerous extensionsno
APIs and other access methodsRESTful HTTP/JSON APIgRPC (using protocol buffers) API
RESTful HTTP/JSON API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
gRPC
OpenAPI 3.0
RESTful HTTP/JSON API infoOpenAPI 3.0
Supported programming languages.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
C
C++
Delphi
Java infoJDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl
.Net
Go
Java
JavaScript (Node.js)
Python
Rust
Server-side scripts infoStored proceduresuser defined functions infoin JavaScriptusing Google App Engineuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.
TriggersnoCallbacks using the Google Apps Engineyes
Partitioning methods infoMethods for storing different data on different nodesnoneShardingpartitioning by range, list and (since PostgreSQL 11) by hashSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using PaxosSource-replica replication infoother methods possible by using 3rd party extensionsCollection-level replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflownono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Immediate ConsistencyEventual Consistency, tunable consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infoSince BigQuery is designed for querying dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACID
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.nononoyes
User concepts infoAccess controlAccess 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)fine grained access rights according to SQL-standardKey-based authentication

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More resources
Google BigQueryGoogle Cloud DatastorePostgreSQLQdrant
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