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DBMS > Apache Druid vs. GigaSpaces vs. Hazelcast vs. Teradata Aster

System Properties Comparison Apache Druid vs. GigaSpaces vs. Hazelcast vs. Teradata Aster

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonGigaSpaces  Xexclude from comparisonHazelcast  Xexclude from comparisonTeradata Aster  Xexclude from comparison
Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataHigh performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented TransactionsA widely adopted in-memory data gridPlatform for big data analytics on multistructured data sources and types
Primary database modelRelational DBMS
Time Series DBMS
Document store
Object oriented DBMS infoValues are user defined objects
Key-value storeRelational DBMS
Secondary database modelsGraph DBMS
Search engine
Document store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.29
Rank#95  Overall
#49  Relational DBMS
#7  Time Series DBMS
Score1.02
Rank#192  Overall
#32  Document stores
#6  Object oriented DBMS
Score6.87
Rank#55  Overall
#6  Key-value stores
Websitedruid.apache.orgwww.gigaspaces.comhazelcast.com
Technical documentationdruid.apache.org/­docs/­latest/­designdocs.gigaspaces.com/­latest/­landing.htmlhazelcast.org/­imdg/­docs
DeveloperApache Software Foundation and contributorsGigaspaces TechnologiesHazelcastTeradata
Initial release2012200020082005
Current release29.0.1, April 202415.5, September 20205.3.6, November 2023
License infoCommercial or Open SourceOpen Source infoApache license v2Open Source infoApache Version 2; Commercial licenses availableOpen Source infoApache Version 2; commercial licenses availablecommercial
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJava, C++, .NetJava
Server operating systemsLinux
OS X
Unix
Linux
macOS
Solaris
Windows
All OS with a Java VMLinux
Data schemeyes infoschema-less columns are supportedschema-freeschema-freeFlexible Schema (defined schema, partial schema, schema free) infodefined schema within the relational store; partial schema or schema free in the Aster File Store
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.nono infoXML can be used for describing objects metadatayes infothe object must implement a serialization strategyyes infoin Aster File Store
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSQL for queryingSQL-99 for query and DML statementsSQL-like query languageyes
APIs and other access methodsJDBC
RESTful HTTP/JSON API
GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data
JCache
JPA
Memcached protocol
RESTful HTTP API
ADO.NET
JDBC
ODBC
OLE DB
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
.Net
C++
Java
Python
Scala
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresnoyesyes infoEvent Listeners, Executor ServicesR packages
Triggersnoyes, event driven architectureyes infoEventsno
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesMulti-source replication infosynchronous or asynchronous
Source-replica replication infosynchronous or asynchronous
yes infoReplicated Mapyes infoDimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infoMap-Reduce pattern can be built with XAP task executorsyesyes infoSQL Map-Reduce Framework
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency infoConsistency level configurable: ALL, QUORUM, ANYImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDone or two-phase-commit; repeatable reads; read commitedACID
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 systemRole-based access controlRole-based access controlfine grained access rights according to SQL-standard

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More resources
Apache DruidGigaSpacesHazelcastTeradata Aster
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