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DBMS > Apache Impala vs. Infobright vs. Microsoft Azure Data Explorer vs. ReductStore

System Properties Comparison Apache Impala vs. Infobright vs. Microsoft Azure Data Explorer vs. ReductStore

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonInfobright  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonReductStore  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopHigh performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontendFully managed big data interactive analytics platformDesigned to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.
Primary database modelRelational DBMSRelational DBMSRelational DBMS infocolumn orientedTime Series DBMS
Secondary database modelsDocument storeDocument 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
Score10.63
Rank#40  Overall
#24  Relational DBMS
Score0.88
Rank#198  Overall
#92  Relational DBMS
Score3.28
Rank#83  Overall
#45  Relational DBMS
Score0.00
Rank#385  Overall
#40  Time Series DBMS
Websiteimpala.apache.orgignitetech.com/­softwarelibrary/­infobrightdbazure.microsoft.com/­services/­data-explorergithub.com/­reductstore
www.reduct.store
Technical documentationimpala.apache.org/­impala-docs.htmldocs.microsoft.com/­en-us/­azure/­data-explorerwww.reduct.store/­docs
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaIgnite Technologies Inc.; formerly InfoBright Inc.MicrosoftReductStore LLC
Initial release2013200520192023
Current release4.1.0, June 2022cloud service with continuous releases1.9, March 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2commercial infoThe open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016commercialOpen Source infoBusiness Source License 1.1
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++CC++, Rust
Server operating systemsLinuxLinux
Windows
hostedDocker
Linux
macOS
Windows
Data schemeyesyesFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesyesyes 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
Secondary indexesyesno infoKnowledge Grid Technology used insteadall fields are automatically indexed
SQL infoSupport of SQLSQL-like DML and DDL statementsyesKusto Query Language (KQL), SQL subset
APIs and other access methodsJDBC
ODBC
ADO.NET
JDBC
ODBC
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
HTTP API
Supported programming languagesAll languages supporting JDBC/ODBC.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C++
JavaScript (Node.js)
Python
Rust
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenoYes, possible languages: KQL, Python, R
Triggersnonoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesShardingnoneSharding 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 ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDno
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.noyesno
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosfine grained access rights according to SQL-standard infoexploiting MySQL or PostgreSQL frontend capabilitiesAzure Active Directory Authentication

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More resources
Apache ImpalaInfobrightMicrosoft Azure Data ExplorerReductStore
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