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DBMS > Apache Impala vs. Apache IoTDB vs. Drizzle vs. Kinetica vs. Microsoft SQL Server

System Properties Comparison Apache Impala vs. Apache IoTDB vs. Drizzle vs. Kinetica vs. Microsoft SQL Server

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonApache IoTDB  Xexclude from comparisonDrizzle  Xexclude from comparisonKinetica  Xexclude from comparisonMicrosoft SQL Server  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.
DescriptionAnalytic DBMS for HadoopAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Fully vectorized database across both GPUs and CPUsMicrosofts flagship relational DBMS
Primary database modelRelational DBMSTime Series DBMSRelational DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument storeSpatial DBMS
Time Series DBMS
Document store
Graph DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score15.06
Rank#39  Overall
#24  Relational DBMS
Score1.19
Rank#183  Overall
#15  Time Series DBMS
Score0.74
Rank#229  Overall
#105  Relational DBMS
Score845.81
Rank#3  Overall
#3  Relational DBMS
Websiteimpala.apache.orgiotdb.apache.orgwww.kinetica.comwww.microsoft.com/­en-us/­sql-server
Technical documentationimpala.apache.org/­impala-docs.htmliotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.kinetica.comlearn.microsoft.com/­en-US/­sql/­sql-server
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaApache Software FoundationDrizzle project, originally started by Brian AkerKineticaMicrosoft
Initial release20132018200820121989
Current release4.1.0, June 20221.1.0, April 20237.2.4, September 20127.1, August 2021SQL Server 2022, November 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache Version 2.0Open Source infoGNU GPLcommercialcommercial inforestricted free version is available
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++JavaC++C, C++C++
Server operating systemsLinuxAll OS with a Java VM (>= 1.8)FreeBSD
Linux
OS X
LinuxLinux
Windows
Data schemeyesyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyesyes
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.nononoyes
Secondary indexesyesyesyesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query languageyes infowith proprietary extensionsSQL-like DML and DDL statementsyes
APIs and other access methodsJDBC
ODBC
JDBC
Native API
JDBCJDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)
Supported programming languagesAll languages supporting JDBC/ODBCC
C#
C++
Go
Java
Python
Scala
C
C++
Java
PHP
C++
Java
JavaScript (Node.js)
Python
C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyesnouser defined functionsTransact SQL, .NET languages, R, Python and (with SQL Server 2019) Java
Triggersnoyesno infohooks for callbacks inside the server can be used.yes infotriggers when inserted values for one or more columns fall within a specified rangeyes
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingShardingtables can be distributed across several files (horizontal partitioning); sharding through federation
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasMulti-source replication
Source-replica replication
Source-replica replicationyes, but depending on the SQL-Server Edition
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceIntegration with Hadoop and Sparknonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual Consistency
Strong Consistency with Raft
Immediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integritynonoyesyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
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.noyesyes infoGPU vRAM or System RAMyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosyesPluggable authentication mechanisms infoe.g. LDAP, HTTPAccess rights for users and roles on table levelfine grained access rights according to SQL-standard

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More resources
Apache ImpalaApache IoTDBDrizzleKineticaMicrosoft SQL Server
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