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DBMS > GeoMesa vs. GigaSpaces vs. Hive vs. Microsoft Azure Data Explorer

System Properties Comparison GeoMesa vs. GigaSpaces vs. Hive vs. Microsoft Azure Data Explorer

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Editorial information provided by DB-Engines
NameGeoMesa  Xexclude from comparisonGigaSpaces  Xexclude from comparisonHive  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionGeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactionsdata warehouse software for querying and managing large distributed datasets, built on HadoopFully managed big data interactive analytics platform
Primary database modelSpatial DBMSDocument store
Object oriented DBMS infoValues are user defined objects
Relational DBMSRelational DBMS infocolumn oriented
Secondary database modelsGraph DBMS
Search engine
Document 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
Score0.86
Rank#205  Overall
#4  Spatial DBMS
Score1.03
Rank#188  Overall
#32  Document stores
#6  Object oriented DBMS
Score59.76
Rank#18  Overall
#12  Relational DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Websitewww.geomesa.orgwww.gigaspaces.comhive.apache.orgazure.microsoft.com/­services/­data-explorer
Technical documentationwww.geomesa.org/­documentation/­stable/­user/­index.htmldocs.gigaspaces.com/­latest/­landing.htmlcwiki.apache.org/­confluence/­display/­Hive/­Homedocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperCCRi and othersGigaspaces TechnologiesApache Software Foundation infoinitially developed by FacebookMicrosoft
Initial release2014200020122019
Current release5.0.0, May 202415.5, September 20203.1.3, April 2022cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache License 2.0Open Source infoApache Version 2; Commercial licenses availableOpen Source infoApache Version 2commercial
Cloud-based only infoOnly available as a cloud servicenononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScalaJava, C++, .NetJava
Server operating systemsLinux
macOS
Solaris
Windows
All OS with a Java VMhosted
Data schemeyesschema-freeyesFixed 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.nono infoXML can be used for describing objects metadatayes
Secondary indexesyesyesyesall fields are automatically indexed
SQL infoSupport of SQLnoSQL-99 for query and DML statementsSQL-like DML and DDL statementsKusto Query Language (KQL), SQL subset
APIs and other access methodsGigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data
JDBC
ODBC
Thrift
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languages.Net
C++
Java
Python
Scala
C++
Java
PHP
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresnoyesyes infouser defined functions and integration of map-reduceYes, possible languages: KQL, Python, R
Triggersnoyes, event driven architecturenoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesdepending on storage layerShardingShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesdepending on storage layerMulti-source replication infosynchronous or asynchronous
Source-replica replication infosynchronous or asynchronous
selectable replication factoryes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyes infoMap-Reduce pattern can be built with XAP task executorsyes infoquery execution via MapReduceSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemdepending on storage layerImmediate Consistency infoConsistency level configurable: ALL, QUORUM, ANYEventual ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.depending on storage layeryesno
User concepts infoAccess controlyes infodepending on the DBMS used for storageRole-based access controlAccess rights for users, groups and rolesAzure Active Directory Authentication

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More resources
GeoMesaGigaSpacesHiveMicrosoft Azure Data Explorer
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