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DBMS > Apache Druid vs. GridGain vs. Hazelcast vs. Microsoft Azure Table Storage

System Properties Comparison Apache Druid vs. GridGain vs. Hazelcast vs. Microsoft Azure Table Storage

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonGridGain  Xexclude from comparisonHazelcast  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataGridGain is an in-memory computing platform, built on Apache IgniteA widely adopted in-memory data gridA Wide Column Store for rapid development using massive semi-structured datasets
Primary database modelRelational DBMS
Time Series DBMS
Key-value store
Relational DBMS
Key-value storeWide column store
Secondary database modelsDocument store infoJSON support with IMDG 3.12
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
Score1.47
Rank#154  Overall
#26  Key-value stores
#72  Relational DBMS
Score5.97
Rank#57  Overall
#6  Key-value stores
Score4.48
Rank#75  Overall
#6  Wide column stores
Websitedruid.apache.orgwww.gridgain.comhazelcast.comazure.microsoft.com/­en-us/­services/­storage/­tables
Technical documentationdruid.apache.org/­docs/­latest/­designwww.gridgain.com/­docs/­index.htmlhazelcast.org/­imdg/­docs
DeveloperApache Software Foundation and contributorsGridGain Systems, Inc.HazelcastMicrosoft
Initial release2012200720082012
Current release29.0.1, April 2024GridGain 8.5.15.3.6, November 2023
License infoCommercial or Open SourceOpen Source infoApache license v2commercialOpen Source infoApache Version 2; commercial licenses availablecommercial
Cloud-based only infoOnly available as a cloud servicenononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJava, C++, .NetJava
Server operating systemsLinux
OS X
Unix
Linux
OS X
Solaris
Windows
All OS with a Java VMhosted
Data schemeyes infoschema-less columns are supportedyesschema-freeschema-free
Typing infopredefined data types such as float or dateyesyesyesyes
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.noyesyes infothe object must implement a serialization strategyno
Secondary indexesyesyesyesno
SQL infoSupport of SQLSQL for queryingANSI-99 for query and DML statements, subset of DDLSQL-like query languageno
APIs and other access methodsJDBC
RESTful HTTP/JSON API
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
JCache
JPA
Memcached protocol
RESTful HTTP API
RESTful HTTP API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
C#
C++
Java
PHP
Python
Ruby
Scala
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresnoyes (compute grid and cache interceptors can be used instead)yes infoEvent Listeners, Executor Servicesno
Triggersnoyes (cache interceptors and events)yes infoEventsno
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesyes (replicated cache)yes infoReplicated Mapyes 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)yesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDone or two-phase-commit; repeatable reads; read commitedoptimistic locking
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.noyesyesno
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemSecurity Hooks for custom implementationsRole-based access controlAccess rights based on private key authentication or shared access signatures

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More resources
Apache DruidGridGainHazelcastMicrosoft Azure Table Storage
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