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DBMS > Databricks vs. GridGain vs. MarkLogic vs. Microsoft Access vs. Sphinx

System Properties Comparison Databricks vs. GridGain vs. MarkLogic vs. Microsoft Access vs. Sphinx

Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonGridGain  Xexclude from comparisonMarkLogic  Xexclude from comparisonMicrosoft Access  Xexclude from comparisonSphinx  Xexclude from comparison
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.GridGain is an in-memory computing platform, built on Apache IgniteOperational and transactional Enterprise NoSQL databaseMicrosoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries. infoThe Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)Open source search engine for searching in data from different sources, e.g. relational databases
Primary database modelDocument store
Relational DBMS
Key-value store
Relational DBMS
Document store
Native XML DBMS
RDF store infoas of version 7
Search engine
Relational DBMSSearch engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score1.47
Rank#154  Overall
#26  Key-value stores
#72  Relational DBMS
Score5.92
Rank#58  Overall
#10  Document stores
#1  Native XML DBMS
#1  RDF stores
#6  Search engines
Score104.92
Rank#11  Overall
#8  Relational DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Websitewww.databricks.comwww.gridgain.comwww.marklogic.comwww.microsoft.com/­en-us/­microsoft-365/­accesssphinxsearch.com
Technical documentationdocs.databricks.comwww.gridgain.com/­docs/­index.htmldocs.marklogic.comdeveloper.microsoft.com/­en-us/­accesssphinxsearch.com/­docs
DeveloperDatabricksGridGain Systems, Inc.MarkLogic Corp.MicrosoftSphinx Technologies Inc.
Initial release20132007200119922001
Current releaseGridGain 8.5.111.0, December 20221902 (16.0.11328.20222), March 20193.5.1, February 2023
License infoCommercial or Open Sourcecommercialcommercialcommercial inforestricted free version is availablecommercial infoBundled with Microsoft OfficeOpen Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud serviceyesnononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, C++, .NetC++C++C++
Server operating systemshostedLinux
OS X
Solaris
Windows
Linux
OS X
Windows
Windows infoNot a real database server, but making use of DLLsFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesschema-free infoSchema can be enforcedyesyes
Typing infopredefined data types such as float or dateyesyesyesno
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.yesyesyes
Secondary indexesyesyesyesyesyes infofull-text index on all search fields
SQL infoSupport of SQLwith Databricks SQLANSI-99 for query and DML statements, subset of DDLyes infoSQL92yes infobut not compliant to any SQL standardSQL-like query language (SphinxQL)
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
Java API
Node.js Client API
ODBC
proprietary Optic API infoProprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT
ADO.NET
DAO
ODBC
OLE DB
Proprietary protocol
Supported programming languagesPython
R
Scala
C#
C++
Java
PHP
Python
Ruby
Scala
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresuser defined functions and aggregatesyes (compute grid and cache interceptors can be used instead)yes infovia XQuery or JavaScriptyes infosince Access 2010 using the ACE-engineno
Triggersyes (cache interceptors and events)yesyes infosince Access 2010 using the ACE-engineno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingnoneSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesyesyes (replicated cache)yesnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes (compute grid and hadoop accelerator)yes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACID infocan act as a resource manager in an XA/JTA transactionACID infobut no files for transaction loggingno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infobut no files for transaction loggingyes infoThe original contents of fields are not stored in the Sphinx index.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes, with Range Indexes
User concepts infoAccess controlSecurity Hooks for custom implementationsRole-based access control at the document and subdocument levelsno infoa simple user-level security was built in till version Access 2003no
More information provided by the system vendor
DatabricksGridGainMarkLogicMicrosoft AccessSphinx
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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DatabricksGridGainMarkLogicMicrosoft AccessSphinx
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