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DBMS > Elasticsearch vs. GridGain vs. Microsoft Azure Table Storage vs. Teradata Aster

System Properties Comparison Elasticsearch vs. GridGain vs. Microsoft Azure Table Storage vs. Teradata Aster

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Editorial information provided by DB-Engines
NameElasticsearch  Xexclude from comparisonGridGain  Xexclude from comparisonMicrosoft Azure Table Storage  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.
DescriptionA distributed, RESTful modern search and analytics engine based on Apache Lucene infoElasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metricGridGain is an in-memory computing platform, built on Apache IgniteA Wide Column Store for rapid development using massive semi-structured datasetsPlatform for big data analytics on multistructured data sources and types
Primary database modelSearch engineKey-value store
Relational DBMS
Wide column storeRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
Vector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score135.35
Rank#7  Overall
#1  Search engines
Score1.47
Rank#154  Overall
#26  Key-value stores
#72  Relational DBMS
Score4.48
Rank#75  Overall
#6  Wide column stores
Websitewww.elastic.co/­elasticsearchwww.gridgain.comazure.microsoft.com/­en-us/­services/­storage/­tables
Technical documentationwww.elastic.co/­guide/­en/­elasticsearch/­reference/­current/­index.htmlwww.gridgain.com/­docs/­index.html
DeveloperElasticGridGain Systems, Inc.MicrosoftTeradata
Initial release2010200720122005
Current release8.6, January 2023GridGain 8.5.1
License infoCommercial or Open SourceOpen Source infoElastic Licensecommercialcommercialcommercial
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJava, C++, .Net
Server operating systemsAll OS with a Java VMLinux
OS X
Solaris
Windows
hostedLinux
Data schemeschema-free infoFlexible type definitions. Once a type is defined, it is persistentyesschema-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.noyesnoyes infoin Aster File Store
Secondary indexesyes infoAll search fields are automatically indexedyesnoyes
SQL infoSupport of SQLSQL-like query languageANSI-99 for query and DML statements, subset of DDLnoyes
APIs and other access methodsJava API
RESTful HTTP/JSON API
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
RESTful HTTP APIADO.NET
JDBC
ODBC
OLE DB
Supported programming languages.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby
C#
C++
Java
PHP
Python
Ruby
Scala
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresyesyes (compute grid and cache interceptors can be used instead)noR packages
Triggersyes infoby using the 'percolation' featureyes (cache interceptors and events)nono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoImplicit feature of the cloud serviceSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesyes (replicated cache)yes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes 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 methodsES-Hadoop Connectoryes (compute grid and hadoop accelerator)noyes infoSQL Map-Reduce Framework
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infoSynchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, allImmediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDoptimistic lockingACID
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.Memcached and Redis integrationyesnono
User concepts infoAccess controlSecurity Hooks for custom implementationsAccess rights based on private key authentication or shared access signaturesfine grained access rights according to SQL-standard

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More resources
ElasticsearchGridGainMicrosoft Azure Table StorageTeradata Aster
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