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DBMS > Databricks vs. GeoSpock vs. GridGain vs. Sphinx vs. Teradata Aster

System Properties Comparison Databricks vs. GeoSpock vs. GridGain vs. Sphinx vs. Teradata Aster

Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonGeoSpock  Xexclude from comparisonGridGain  Xexclude from comparisonSphinx  Xexclude from comparisonTeradata Aster  Xexclude from comparison
GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.
DescriptionThe Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.Spatial and temporal data processing engine for extreme data scaleGridGain is an in-memory computing platform, built on Apache IgniteOpen source search engine for searching in data from different sources, e.g. relational databasesPlatform for big data analytics on multistructured data sources and types
Primary database modelDocument store
Relational DBMS
Relational DBMSKey-value store
Relational DBMS
Search engineRelational DBMS
Secondary database modelsTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score1.55
Rank#150  Overall
#26  Key-value stores
#70  Relational DBMS
Score5.95
Rank#55  Overall
#5  Search engines
Websitewww.databricks.comgeospock.comwww.gridgain.comsphinxsearch.com
Technical documentationdocs.databricks.comwww.gridgain.com/­docs/­index.htmlsphinxsearch.com/­docs
DeveloperDatabricksGeoSpockGridGain Systems, Inc.Sphinx Technologies Inc.Teradata
Initial release2013200720012005
Current release2.0, September 2019GridGain 8.5.13.5.1, February 2023
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infoGPL version 2, commercial licence availablecommercial
Cloud-based only infoOnly available as a cloud serviceyesyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, JavascriptJava, C++, .NetC++
Server operating systemshostedhostedLinux
OS X
Solaris
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesyesyesFlexible 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 dateyesyesnoyes
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.yesnoyesyes infoin Aster File Store
Secondary indexesyestemporal, categoricalyesyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLwith Databricks SQLANSI SQL for query only (using Presto)ANSI-99 for query and DML statements, subset of DDLSQL-like query language (SphinxQL)yes
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBCHDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
Proprietary protocolADO.NET
JDBC
ODBC
OLE DB
Supported programming languagesPython
R
Scala
C#
C++
Java
PHP
Python
Ruby
Scala
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresuser defined functions and aggregatesnoyes (compute grid and cache interceptors can be used instead)noR packages
Triggersnoyes (cache interceptors and events)nono
Partitioning methods infoMethods for storing different data on different nodesAutomatic shardingShardingSharding infoPartitioning is done manually, search queries against distributed index is supportedSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesyes (replicated cache)noneyes 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 (compute grid and hadoop accelerator)noyes infoSQL Map-Reduce Framework
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate 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 dataACIDnoACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesno
User concepts infoAccess controlAccess rights for users can be defined per tableSecurity Hooks for custom implementationsnofine grained access rights according to SQL-standard
More information provided by the system vendor
DatabricksGeoSpockGridGainSphinxTeradata Aster
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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DatabricksGeoSpockGridGainSphinxTeradata Aster
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