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DBMS > Apache Pinot vs. Microsoft Azure Data Explorer vs. SAP HANA

System Properties Comparison Apache Pinot vs. Microsoft Azure Data Explorer vs. SAP HANA

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Editorial information provided by DB-Engines
NameApache Pinot  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSAP HANA  Xexclude from comparison
DescriptionRealtime distributed OLAP datastore, designed to answer OLAP queries with low latencyFully managed big data interactive analytics platformIn-memory, column based data store. Available as appliance or cloud service
Primary database modelRelational DBMSRelational DBMS infocolumn orientedRelational DBMS
Secondary database modelsDocument 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
Document store
Graph DBMS infowith SAP Hana, Enterprise Edition
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.40
Rank#270  Overall
#125  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score44.69
Rank#22  Overall
#16  Relational DBMS
Websitepinot.apache.orgazure.microsoft.com/­services/­data-explorerwww.sap.com/­products/­hana.html
Technical documentationdocs.pinot.apache.orgdocs.microsoft.com/­en-us/­azure/­data-explorerhelp.sap.com/­hana
DeveloperApache Software Foundation and contributorsMicrosoftSAP
Initial release201520192010
Current release1.0.0, September 2023cloud service with continuous releases2.0 SPS07 (AprilĀ 4, 2023), April 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercial
Cloud-based only infoOnly available as a cloud servicenoyesno infoalso available as a cloud based service
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava
Server operating systemsAll OS with a Java JDK11 or higherhostedAppliance or cloud-service
Data schemeyesFixed schema with schema-less datatypes (dynamic)yes
Typing infopredefined data types such as float or dateyesyes 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.yesno
Secondary indexesall fields are automatically indexedyes
SQL infoSupport of SQLSQL-like query languageKusto Query Language (KQL), SQL subsetyes
APIs and other access methodsJDBCMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
JDBC
ODBC
Supported programming languagesGo
Java
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresYes, possible languages: KQL, Python, RSQLScript, R
Triggersyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningSharding infoImplicit feature of the cloud serviceyes
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes
MapReduce infoOffers an API for user-defined Map/Reduce methodsSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controlAzure Active Directory Authenticationyes

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More resources
Apache PinotMicrosoft Azure Data ExplorerSAP HANA
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