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DBMS > Apache Impala vs. Apache IoTDB vs. Datomic vs. Microsoft Azure Data Explorer

System Properties Comparison Apache Impala vs. Apache IoTDB vs. Datomic vs. Microsoft Azure Data Explorer

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonApache IoTDB  Xexclude from comparisonDatomic  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityFully managed big data interactive analytics platform
Primary database modelRelational DBMSTime Series DBMSRelational DBMSRelational DBMS infocolumn oriented
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
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score1.66
Rank#144  Overall
#66  Relational DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Websiteimpala.apache.orgiotdb.apache.orgwww.datomic.comazure.microsoft.com/­services/­data-explorer
Technical documentationimpala.apache.org/­impala-docs.htmliotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.datomic.comdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaApache Software FoundationCognitectMicrosoft
Initial release2013201820122019
Current release4.1.0, June 20221.1.0, April 20231.0.7075, December 2023cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache Version 2.0commercial infolimited edition freecommercial
Cloud-based only infoOnly available as a cloud servicenononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++JavaJava, Clojure
Server operating systemsLinuxAll OS with a Java VM (>= 1.8)All OS with a Java VMhosted
Data schemeyesyesyesFixed schema with schema-less datatypes (dynamic)
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-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.nononoyes
Secondary indexesyesyesyesall fields are automatically indexed
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query languagenoKusto Query Language (KQL), SQL subset
APIs and other access methodsJDBC
ODBC
JDBC
Native API
RESTful HTTP APIMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesAll languages supporting JDBC/ODBCC
C#
C++
Go
Java
Python
Scala
Clojure
Java
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyesyes infoTransaction FunctionsYes, possible languages: KQL, Python, R
TriggersnoyesBy using transaction functionsyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioning (by time range) + vertical partitioning (by deviceId)none infoBut extensive use of caching in the application peersSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasnone infoBut extensive use of caching in the application peersyes 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 MapReduceIntegration with Hadoop and SparknoSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes inforecommended only for testing and developmentno
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosyesnoAzure Active Directory Authentication

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Apache ImpalaApache IoTDBDatomicMicrosoft Azure Data Explorer
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