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DBMS > Apache Impala vs. EXASOL vs. Hazelcast vs. Microsoft Azure Data Explorer vs. PostgreSQL

System Properties Comparison Apache Impala vs. EXASOL vs. Hazelcast vs. Microsoft Azure Data Explorer vs. PostgreSQL

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonEXASOL  Xexclude from comparisonHazelcast  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonPostgreSQL  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopHigh-performance, in-memory, MPP database specifically designed for in-memory analytics.A widely adopted in-memory data gridFully managed big data interactive analytics platformWidely used open source RDBMS infoDeveloped as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL
Primary database modelRelational DBMSRelational DBMSKey-value storeRelational DBMS infocolumn orientedRelational DBMS infowith object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.
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 infowith Apache Age
Spatial DBMS
Vector DBMS infowith pgvector extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score1.99
Rank#124  Overall
#58  Relational DBMS
Score5.97
Rank#57  Overall
#6  Key-value stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score645.54
Rank#4  Overall
#4  Relational DBMS
Websiteimpala.apache.orgwww.exasol.comhazelcast.comazure.microsoft.com/­services/­data-explorerwww.postgresql.org
Technical documentationimpala.apache.org/­impala-docs.htmlwww.exasol.com/­resourceshazelcast.org/­imdg/­docsdocs.microsoft.com/­en-us/­azure/­data-explorerwww.postgresql.org/­docs
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaExasolHazelcastMicrosoftPostgreSQL Global Development Group infowww.postgresql.org/­developer
Initial release20132000200820191989 info1989: Postgres, 1996: PostgreSQL
Current release4.1.0, June 20225.3.6, November 2023cloud service with continuous releases16.2, February 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialOpen Source infoApache Version 2; commercial licenses availablecommercialOpen Source infoBSD
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
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Implementation languageC++JavaC
Server operating systemsLinuxAll OS with a Java VMhostedFreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows
Data schemeyesyesschema-freeFixed schema with schema-less datatypes (dynamic)yes
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-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.nonoyes infothe object must implement a serialization strategyyesyes infospecific XML-type available, but no XML query functionality.
Secondary indexesyesyesyesall fields are automatically indexedyes
SQL infoSupport of SQLSQL-like DML and DDL statementsyesSQL-like query languageKusto Query Language (KQL), SQL subsetyes infostandard with numerous extensions
APIs and other access methodsJDBC
ODBC
.Net
JDBC
ODBC
WebSocket
JCache
JPA
Memcached protocol
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesAll languages supporting JDBC/ODBCJava
Lua
Python
R
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net
C
C++
Delphi
Java infoJDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functionsyes infoEvent Listeners, Executor ServicesYes, possible languages: KQL, Python, Ruser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.
Triggersnoyesyes infoEventsyes 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 servicepartitioning by range, list and (since PostgreSQL 11) by hash
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryes infoReplicated Mapyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replication infoother methods possible by using 3rd party extensions
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceyes infoHadoop integrationyesSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyesnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDone or two-phase-commit; repeatable reads; read commitednoACID
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.noyesyesnono
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosAccess rights for users, groups and roles according to SQL-standardRole-based access controlAzure Active Directory Authenticationfine grained access rights according to SQL-standard

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Apache ImpalaEXASOLHazelcastMicrosoft Azure Data ExplorerPostgreSQL
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