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DBMS > Apache Druid vs. GBase vs. GridGain vs. HEAVY.AI vs. Microsoft Azure Data Explorer

System Properties Comparison Apache Druid vs. GBase vs. GridGain vs. HEAVY.AI vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonGBase  Xexclude from comparisonGridGain  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataWidely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.GridGain is an in-memory computing platform, built on Apache IgniteA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareFully managed big data interactive analytics platform
Primary database modelRelational DBMS
Time Series DBMS
Relational DBMSKey-value store
Relational DBMS
Relational DBMSRelational DBMS infocolumn oriented
Secondary database modelsSpatial DBMSDocument 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
Score3.34
Rank#88  Overall
#48  Relational DBMS
#7  Time Series DBMS
Score1.07
Rank#185  Overall
#86  Relational DBMS
Score1.47
Rank#154  Overall
#26  Key-value stores
#72  Relational DBMS
Score1.77
Rank#141  Overall
#65  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Websitedruid.apache.orgwww.gbase.cnwww.gridgain.comgithub.com/­heavyai/­heavydb
www.heavy.ai
azure.microsoft.com/­services/­data-explorer
Technical documentationdruid.apache.org/­docs/­latest/­designwww.gridgain.com/­docs/­index.htmldocs.heavy.aidocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperApache Software Foundation and contributorsGeneral Data Technology Co., Ltd.GridGain Systems, Inc.HEAVY.AI, Inc.Microsoft
Initial release20122004200720162019
Current release29.0.1, April 2024GBase 8a, GBase 8s, GBase 8cGridGain 8.5.15.10, January 2022cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache license v2commercialcommercialOpen Source infoApache Version 2; enterprise edition 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 languageJavaC, Java, PythonJava, C++, .NetC++ and CUDA
Server operating systemsLinux
OS X
Unix
LinuxLinux
OS X
Solaris
Windows
Linuxhosted
Data schemeyes infoschema-less columns are supportedyesyesyesFixed 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.noyesyesnoyes
Secondary indexesyesyesyesnoall fields are automatically indexed
SQL infoSupport of SQLSQL for queryingStandard with numerous extensionsANSI-99 for query and DML statements, subset of DDLyesKusto Query Language (KQL), SQL subset
APIs and other access methodsJDBC
RESTful HTTP/JSON API
ADO.NET
C API
JDBC
ODBC
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
JDBC
ODBC
Thrift
Vega
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
C#C#
C++
Java
PHP
Python
Ruby
Scala
All languages supporting JDBC/ODBC/Thrift
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresnouser defined functionsyes (compute grid and cache interceptors can be used instead)noYes, possible languages: KQL, Python, R
Triggersnoyesyes (cache interceptors and events)noyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedhorizontal partitioning (by range, list and hash) and vertical partitioningShardingSharding infoRound robinSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesyesyes (replicated cache)Multi-source replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes (compute grid and hadoop accelerator)noSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
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 controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemyesSecurity Hooks for custom implementationsfine grained access rights according to SQL-standardAzure Active Directory Authentication

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More resources
Apache DruidGBaseGridGainHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Microsoft Azure Data Explorer
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