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DBMS > Google Cloud Bigtable vs. MarkLogic vs. Microsoft Azure Synapse Analytics vs. PlanetScale

System Properties Comparison Google Cloud Bigtable vs. MarkLogic vs. Microsoft Azure Synapse Analytics vs. PlanetScale

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Editorial information provided by DB-Engines
NameGoogle Cloud Bigtable  Xexclude from comparisonMarkLogic  Xexclude from comparisonMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data Warehouse  Xexclude from comparisonPlanetScale  Xexclude from comparison
DescriptionGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Operational and transactional Enterprise NoSQL databaseElastic, large scale data warehouse service leveraging the broad eco-system of SQL ServerScalable, distributed, serverless MySQL database platform built on top of Vitess
Primary database modelKey-value store
Wide column store
Document store
Native XML DBMS
RDF store infoas of version 7
Search engine
Relational DBMSRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.58
Rank#92  Overall
#14  Key-value stores
#8  Wide column stores
Score6.50
Rank#56  Overall
#10  Document stores
#1  Native XML DBMS
#1  RDF stores
#5  Search engines
Score22.71
Rank#30  Overall
#18  Relational DBMS
Score1.75
Rank#146  Overall
#67  Relational DBMS
Websitecloud.google.com/­bigtablewww.marklogic.comazure.microsoft.com/­services/­synapse-analyticsplanetscale.com
Technical documentationcloud.google.com/­bigtable/­docsdocs.marklogic.comdocs.microsoft.com/­azure/­synapse-analyticsplanetscale.com/­docs
DeveloperGoogleMarkLogic Corp.MicrosoftPlanetScale
Initial release2015200120162020
Current release11.0, December 2022
License infoCommercial or Open Sourcecommercialcommercial inforestricted free version is availablecommercialcommercial
Cloud-based only infoOnly available as a cloud serviceyesnoyesyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++C++Go
Server operating systemshostedLinux
OS X
Windows
hostedDocker
Linux
macOS
Data schemeschema-freeschema-free infoSchema can be enforcedyesyes
Typing infopredefined data types such as float or datenoyesyesyes
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.noyesno
Secondary indexesnoyesyesyes
SQL infoSupport of SQLnoyes infoSQL92yesyes infowith proprietary extensions
APIs and other access methodsgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Java API
Node.js Client API
ODBC
proprietary Optic API infoProprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT
ADO.NET
JDBC
ODBC
ADO.NET
JDBC
MySQL protocol
ODBC
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
Python
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C#
Java
PHP
Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresnoyes infovia XQuery or JavaScriptTransact SQLyes infoproprietary syntax
Triggersnoyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding, horizontal partitioningSharding
Replication methods infoMethods for redundantly storing data on multiple nodesInternal replication in Colossus, and regional replication between two clusters in different zonesyesyesMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate ConsistencyImmediate ConsistencyEventual Consistency across shards
Immediate Consistency within a shard
Foreign keys infoReferential integritynonono infodocs.microsoft.com/­en-us/­azure/­synapse-analytics/­sql-data-warehouse/­sql-data-warehouse-table-constraintsyes infonot for MyISAM storage engine
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-row operationsACID infocan act as a resource manager in an XA/JTA transactionACIDACID at shard level
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes infotable locks or row locks depending on storage engine
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.noyes, with Range Indexesyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Role-based access control at the document and subdocument levelsyesUsers with fine-grained authorization concept infono user groups or roles

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More resources
Google Cloud BigtableMarkLogicMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data WarehousePlanetScale
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