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DBMS > Fujitsu Enterprise Postgres vs. Google Cloud Bigtable vs. MarkLogic vs. Microsoft Azure Data Explorer vs. Microsoft Azure Synapse Analytics

System Properties Comparison Fujitsu Enterprise Postgres vs. Google Cloud Bigtable vs. MarkLogic vs. Microsoft Azure Data Explorer vs. Microsoft Azure Synapse Analytics

Editorial information provided by DB-Engines
NameFujitsu Enterprise Postgres  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonMarkLogic  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data Warehouse  Xexclude from comparison
DescriptionEnterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.Google'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 databaseFully managed big data interactive analytics platformElastic, large scale data warehouse service leveraging the broad eco-system of SQL Server
Primary database modelRelational DBMSKey-value store
Wide column store
Document store
Native XML DBMS
RDF store infoas of version 7
Search engine
Relational DBMS infocolumn orientedRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
Document store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.37
Rank#278  Overall
#128  Relational DBMS
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score5.18
Rank#63  Overall
#11  Document stores
#1  Native XML DBMS
#1  RDF stores
#7  Search engines
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score19.93
Rank#31  Overall
#19  Relational DBMS
Websitewww.postgresql.fastware.comcloud.google.com/­bigtablewww.progress.com/­marklogicazure.microsoft.com/­services/­data-explorerazure.microsoft.com/­services/­synapse-analytics
Technical documentationwww.postgresql.fastware.com/­product-manualscloud.google.com/­bigtable/­docswww.progress.com/­marklogic/­documentationdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.microsoft.com/­azure/­synapse-analytics
DeveloperPostgreSQL Global Development Group, Fujitsu Australia Software TechnologyGoogleMarkLogic Corp.MicrosoftMicrosoft
Initial release2015200120192016
Current releaseFujitsu Enterprise Postgres 14, January 202211.0, December 2022cloud service with continuous releases
License infoCommercial or Open Sourcecommercialcommercialcommercial inforestricted free version is availablecommercialcommercial
Cloud-based only infoOnly available as a cloud servicenoyesnoyesyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageCC++C++
Server operating systemsLinux
Windows
hostedLinux
OS X
Windows
hostedhosted
Data schemeyesschema-freeschema-free infoSchema can be enforcedFixed schema with schema-less datatypes (dynamic)yes
Typing infopredefined data types such as float or dateyesnoyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes
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.noyesyesno
Secondary indexesyesnoyesall fields are automatically indexedyes
SQL infoSupport of SQLyesnoyes infoSQL92Kusto Query Language (KQL), SQL subsetyes
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
gRPC (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
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
ADO.NET
JDBC
ODBC
Supported programming languages.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C#
C++
Go
Java
JavaScript (Node.js)
Python
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C#
Java
PHP
Server-side scripts infoStored proceduresuser defined functionsnoyes infovia XQuery or JavaScriptYes, possible languages: KQL, Python, RTransact SQL
Triggersyesnoyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodespartitioning by range, list and by hashShardingShardingSharding infoImplicit feature of the cloud serviceSharding, horizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationInternal replication in Colossus, and regional replication between two clusters in different zonesyesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integrityyesnononono infodocs.microsoft.com/­en-us/­azure/­synapse-analytics/­sql-data-warehouse/­sql-data-warehouse-table-constraints
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic single-row operationsACID infocan act as a resource manager in an XA/JTA transactionnoACID
Concurrency infoSupport for concurrent manipulation of datayes, multi-version concurrency control (MVCC)yesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes, with Range Indexesno
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Role-based access control at the document and subdocument levelsAzure Active Directory Authenticationyes

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More resources
Fujitsu Enterprise PostgresGoogle Cloud BigtableMarkLogicMicrosoft Azure Data ExplorerMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data Warehouse
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