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DBMS > Apache Impala vs. Hazelcast vs. KeyDB vs. Microsoft Azure Data Explorer vs. Microsoft Azure SQL Database

System Properties Comparison Apache Impala vs. Hazelcast vs. KeyDB vs. Microsoft Azure Data Explorer vs. Microsoft Azure SQL Database

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonHazelcast  Xexclude from comparisonKeyDB  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonMicrosoft Azure SQL Database infoformerly SQL Azure  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopA widely adopted in-memory data gridAn ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocolsFully managed big data interactive analytics platformDatabase as a Service offering with high compatibility to Microsoft SQL Server
Primary database modelRelational DBMSKey-value storeKey-value storeRelational DBMS infocolumn orientedRelational DBMS
Secondary database modelsDocument storeDocument store infoJSON support with IMDG 3.12Document 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
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score5.97
Rank#57  Overall
#6  Key-value stores
Score0.71
Rank#226  Overall
#33  Key-value stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score77.99
Rank#16  Overall
#11  Relational DBMS
Websiteimpala.apache.orghazelcast.comgithub.com/­Snapchat/­KeyDB
keydb.dev
azure.microsoft.com/­services/­data-explorerazure.microsoft.com/­en-us/­products/­azure-sql/­database
Technical documentationimpala.apache.org/­impala-docs.htmlhazelcast.org/­imdg/­docsdocs.keydb.devdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.microsoft.com/­en-us/­azure/­azure-sql
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaHazelcastEQ Alpha Technology Ltd.MicrosoftMicrosoft
Initial release20132008201920192010
Current release4.1.0, June 20225.3.6, November 2023cloud service with continuous releasesV12
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache Version 2; commercial licenses availableOpen Source infoBSD-3commercialcommercial
Cloud-based only infoOnly available as a cloud servicenononoyesyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++JavaC++C++
Server operating systemsLinuxAll OS with a Java VMLinuxhostedhosted
Data schemeyesschema-freeschema-freeFixed schema with schema-less datatypes (dynamic)yes
Typing infopredefined data types such as float or dateyesyespartial infoSupported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexesyes 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.noyes infothe object must implement a serialization strategynoyesyes
Secondary indexesyesyesyes infoby using the Redis Search moduleall fields are automatically indexedyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query languagenoKusto Query Language (KQL), SQL subsetyes
APIs and other access methodsJDBC
ODBC
JCache
JPA
Memcached protocol
RESTful HTTP API
Proprietary protocol infoRESP - REdis Serialization ProtocoMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
ADO.NET
JDBC
ODBC
Supported programming languagesAll languages supporting JDBC/ODBC.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyes infoEvent Listeners, Executor ServicesLuaYes, possible languages: KQL, Python, RTransact SQL
Triggersnoyes infoEventsnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryes infoReplicated MapMulti-source replication
Source-replica replication
yes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes, with always 3 replicas available
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceyesnoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual Consistency
Strong eventual consistency with CRDTs
Eventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynonononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoone or two-phase-commit; repeatable reads; read commitedOptimistic locking, atomic execution of commands blocks and scriptsnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyes infoConfigurable mechanisms for persistency via snapshots and/or operations logsyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyesno
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosRole-based access controlsimple password-based access control and ACLAzure Active Directory Authenticationfine grained access rights according to SQL-standard

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More resources
Apache ImpalaHazelcastKeyDBMicrosoft Azure Data ExplorerMicrosoft Azure SQL Database infoformerly SQL Azure
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