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DBMS > Apache Impala vs. Microsoft Azure Data Explorer vs. OrigoDB vs. Splice Machine

System Properties Comparison Apache Impala vs. Microsoft Azure Data Explorer vs. OrigoDB vs. Splice Machine

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonOrigoDB  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopFully managed big data interactive analytics platformA fully ACID in-memory object graph databaseOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSRelational DBMS infocolumn orientedDocument store
Object oriented DBMS
Relational 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
Score12.45
Rank#40  Overall
#24  Relational DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score0.06
Rank#380  Overall
#50  Document stores
#19  Object oriented DBMS
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websiteimpala.apache.orgazure.microsoft.com/­services/­data-explorerorigodb.comsplicemachine.com
Technical documentationimpala.apache.org/­impala-docs.htmldocs.microsoft.com/­en-us/­azure/­data-explorerorigodb.com/­docssplicemachine.com/­how-it-works
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaMicrosoftRobert Friberg et alSplice Machine
Initial release201320192009 infounder the name LiveDB2014
Current release4.1.0, June 2022cloud service with continuous releases3.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialOpen SourceOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C#Java
Server operating systemsLinuxhostedLinux
Windows
Linux
OS X
Solaris
Windows
Data schemeyesFixed schema with schema-less datatypes (dynamic)yesyes
Typing infopredefined data types such as float or dateyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesUser defined using .NET types and collectionsyes
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.noyesno infocan be achieved using .NET
Secondary indexesyesall fields are automatically indexedyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsKusto Query Language (KQL), SQL subsetnoyes
APIs and other access methodsJDBC
ODBC
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
.NET Client API
HTTP API
LINQ
JDBC
Native Spark Datasource
ODBC
Supported programming languagesAll languages supporting JDBC/ODBC.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.NetC#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceYes, possible languages: KQL, Python, Ryesyes infoJava
Triggersnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infoDomain Eventsyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoImplicit feature of the cloud servicehorizontal partitioning infoclient side managed; servers are not synchronizedShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replicationMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceSpark connector (open source): github.com/­Azure/­azure-kusto-sparknoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynonodepending on modelyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyes infoWrite ahead logyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosAzure Active Directory AuthenticationRole based authorizationAccess rights for users, groups and roles according to SQL-standard

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More resources
Apache ImpalaMicrosoft Azure Data ExplorerOrigoDBSplice Machine
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