DB-EnginesInfluxDB: Focus on building software with an easy-to-use serverless, scalable time series platformEnglish
Deutsch
Knowledge Base of Relational and NoSQL Database Management Systemsprovided by solid IT

DBMS > Apache Impala vs. EXASOL vs. Fujitsu Enterprise Postgres vs. Hazelcast vs. Microsoft Azure Data Explorer

System Properties Comparison Apache Impala vs. EXASOL vs. Fujitsu Enterprise Postgres vs. Hazelcast vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonEXASOL  Xexclude from comparisonFujitsu Enterprise Postgres  Xexclude from comparisonHazelcast  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopHigh-performance, in-memory, MPP database specifically designed for in-memory analytics.Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.A widely adopted in-memory data gridFully managed big data interactive analytics platform
Primary database modelRelational DBMSRelational DBMSRelational DBMSKey-value storeRelational DBMS infocolumn oriented
Secondary database modelsDocument storeDocument store
Spatial DBMS
Document 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
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
Score0.31
Rank#285  Overall
#129  Relational DBMS
Score5.97
Rank#57  Overall
#6  Key-value stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Websiteimpala.apache.orgwww.exasol.comwww.postgresql.fastware.comhazelcast.comazure.microsoft.com/­services/­data-explorer
Technical documentationimpala.apache.org/­impala-docs.htmlwww.exasol.com/­resourceswww.postgresql.fastware.com/­product-manualshazelcast.org/­imdg/­docsdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaExasolPostgreSQL Global Development Group, Fujitsu Australia Software TechnologyHazelcastMicrosoft
Initial release2013200020082019
Current release4.1.0, June 2022Fujitsu Enterprise Postgres 14, January 20225.3.6, November 2023cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercialOpen Source infoApache Version 2; commercial licenses availablecommercial
Cloud-based only infoOnly available as a cloud servicenonononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++CJava
Server operating systemsLinuxLinux
Windows
All OS with a Java VMhosted
Data schemeyesyesyesschema-freeFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesyesyesyesyes 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.nonoyes infothe object must implement a serialization strategyyes
Secondary indexesyesyesyesyesall fields are automatically indexed
SQL infoSupport of SQLSQL-like DML and DDL statementsyesyesSQL-like query languageKusto Query Language (KQL), SQL subset
APIs and other access methodsJDBC
ODBC
.Net
JDBC
ODBC
WebSocket
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JCache
JPA
Memcached protocol
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesAll languages supporting JDBC/ODBCJava
Lua
Python
R
.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functionsuser defined functionsyes infoEvent Listeners, Executor ServicesYes, possible languages: KQL, Python, R
Triggersnoyesyesyes infoEventsyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesShardingShardingpartitioning by range, list and by hashShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replicationyes infoReplicated Mapyes 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 MapReduceyes infoHadoop integrationnoyesSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyesyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDone or two-phase-commit; repeatable reads; read commitedno
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)yesyes
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.noyesyesno
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-standardfine grained access rights according to SQL-standardRole-based access controlAzure Active Directory Authentication
More information provided by the system vendor
Apache ImpalaEXASOLFujitsu Enterprise PostgresHazelcastMicrosoft Azure Data Explorer
Specific characteristics100% compatible with community PostgreSQL
» more
Competitive advantagesBuilt-in TDE and Data Masking security. In-Memory Columnar Index, and a high speed...
» more
Typical application scenariosTransactional payments applications, reporting and mixed workloads.
» more
Market metricsOver 30 years experience in database technology. Over 20 years in Postgres development...
» more
Licensing and pricing modelsCore based licensing
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources
Apache ImpalaEXASOLFujitsu Enterprise PostgresHazelcastMicrosoft Azure Data Explorer
Recent citations in the news

Cloudera creates observability tool to help enterprises manage cloud costs
6 June 2023, SiliconANGLE News

Apache Impala 4 Supports Operator Multi-Threading
29 July 2021, iProgrammer

Apache Impala becomes Top-Level Project
28 November 2017, SDTimes.com

Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

Apache Doris just 'graduated': Why care about this SQL data warehouse
24 June 2022, InfoWorld

provided by Google News

Exasol Finds AI Underinvestment Leads to Business Failure, But Data Challenges Stall Rapid Adoption
20 March 2024, Business Wire

It's Back to the Database Future for Exasol CEO Tewes
26 October 2023, Datanami

Exasol gets jolt of AI with Espresso suite of capabilities
26 February 2024, TechTarget

Exasol Unveils New Suite of AI Tools to Turbocharge Enterprise Data Analytics
21 February 2024, Business Wire

Exasol brings SaaS-flex to on-prem and public cloud systems
31 May 2023, The Register

provided by Google News

Fujitsu Develops Column-Oriented Data-Processing Engine Enabling Fast, High-Volume Data Analysis in Database ...
26 February 2015, Fujitsu

Supporting Business Continuity with Postgres on IBM Cloud LinuxONE Virtual Servers for VPC
24 September 2021, ibm.com

provided by Google News

Hazelcast Weaves Wider Logic Threads Through The Data Fabric
7 March 2024, Forbes

Hazelcast 5.4 real time data processing platform boosts AI and consistency
17 April 2024, VentureBeat

Hazelcast Achieves Record Year with Leading Brands Choosing Its Platform for Application Modernization, AI Initiatives
22 February 2024, Datanami

Real-Time Data Platform Hazelcast Introduces New Chief Technology Officer Adrian Soars
7 November 2023, Finovate

Hazelcast Versus Redis: A Practical Comparison
4 January 2024, Database Trends and Applications

provided by Google News

Providing modern data transfer and storage service at Microsoft with Microsoft Azure - Inside Track Blog
13 July 2023, Microsoft

Introducing Microsoft Fabric: The data platform for the era of AI | Microsoft Azure Blog
23 May 2023, Microsoft

Azure Data Explorer: Log and telemetry analytics benchmark
16 August 2022, Microsoft

Microsoft Introduces Azure Integration Environments and Business Process Tracking in Public Preview
23 November 2023, InfoQ.com

Azure Data Explorer and Stream Analytics for anomaly detection
16 January 2020, Microsoft

provided by Google News



Share this page

Featured Products

RaimaDB logo

RaimaDB, embedded database for mission-critical applications. When performance, footprint and reliability matters.
Try RaimaDB for free.

SingleStore logo

Database for your real-time AI and Analytics Apps.
Try it today.

Neo4j logo

See for yourself how a graph database can make your life easier.
Use Neo4j online for free.

Milvus logo

Vector database designed for GenAI, fully equipped for enterprise implementation.
Try Managed Milvus for Free

Datastax Astra logo

Bring all your data to Generative AI applications with vector search enabled by the most scalable
vector database available.
Try for Free

Present your product here