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DBMS > Apache Pinot vs. Hive vs. Kinetica vs. Microsoft Azure Data Explorer

System Properties Comparison Apache Pinot vs. Hive vs. Kinetica vs. Microsoft Azure Data Explorer

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Editorial information provided by DB-Engines
NameApache Pinot  Xexclude from comparisonHive  Xexclude from comparisonKinetica  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionRealtime distributed OLAP datastore, designed to answer OLAP queries with low latencydata warehouse software for querying and managing large distributed datasets, built on HadoopFully vectorized database across both GPUs and CPUsFully managed big data interactive analytics platform
Primary database modelRelational DBMSRelational DBMSRelational DBMSRelational DBMS infocolumn oriented
Secondary database modelsSpatial DBMS
Time Series 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
Score0.40
Rank#270  Overall
#125  Relational DBMS
Score61.17
Rank#18  Overall
#12  Relational DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Websitepinot.apache.orghive.apache.orgwww.kinetica.comazure.microsoft.com/­services/­data-explorer
Technical documentationdocs.pinot.apache.orgcwiki.apache.org/­confluence/­display/­Hive/­Homedocs.kinetica.comdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperApache Software Foundation and contributorsApache Software Foundation infoinitially developed by FacebookKineticaMicrosoft
Initial release2015201220122019
Current release1.0.0, September 20233.1.3, April 20227.1, August 2021cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2commercialcommercial
Cloud-based only infoOnly available as a cloud servicenononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaC, C++
Server operating systemsAll OS with a Java JDK11 or higherAll OS with a Java VMLinuxhosted
Data schemeyesyesyesFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types
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.noyes
Secondary indexesyesyesall fields are automatically indexed
SQL infoSupport of SQLSQL-like query languageSQL-like DML and DDL statementsSQL-like DML and DDL statementsKusto Query Language (KQL), SQL subset
APIs and other access methodsJDBCJDBC
ODBC
Thrift
JDBC
ODBC
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesGo
Java
Python
C++
Java
PHP
Python
C++
Java
JavaScript (Node.js)
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functionsYes, possible languages: KQL, Python, R
Triggersnoyes infotriggers when inserted values for one or more columns fall within a specified rangeyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningShardingShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenoSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency or Eventual Consistency depending on configurationEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonono
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes infoGPU vRAM or System RAMno
User concepts infoAccess controlAccess rights for users, groups and rolesAccess rights for users and roles on table levelAzure Active Directory Authentication

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More resources
Apache PinotHiveKineticaMicrosoft Azure Data Explorer
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