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DBMS > Apache Pinot vs. MarkLogic vs. Microsoft Access vs. Spark SQL vs. Teradata Aster

System Properties Comparison Apache Pinot vs. MarkLogic vs. Microsoft Access vs. Spark SQL vs. Teradata Aster

Editorial information provided by DB-Engines
NameApache Pinot  Xexclude from comparisonMarkLogic  Xexclude from comparisonMicrosoft Access  Xexclude from comparisonSpark SQL  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.
DescriptionRealtime distributed OLAP datastore, designed to answer OLAP queries with low latencyOperational 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.)Spark SQL is a component on top of 'Spark Core' for structured data processingPlatform for big data analytics on multistructured data sources and types
Primary database modelRelational DBMSDocument store
Native XML DBMS
RDF store infoas of version 7
Search engine
Relational DBMSRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.38
Rank#275  Overall
#126  Relational DBMS
Score5.18
Rank#63  Overall
#11  Document stores
#1  Native XML DBMS
#1  RDF stores
#7  Search engines
Score101.16
Rank#11  Overall
#8  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitepinot.apache.orgwww.progress.com/­marklogicwww.microsoft.com/­en-us/­microsoft-365/­accessspark.apache.org/­sql
Technical documentationdocs.pinot.apache.orgwww.progress.com/­marklogic/­documentationdeveloper.microsoft.com/­en-us/­accessspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software Foundation and contributorsMarkLogic Corp.MicrosoftApache Software FoundationTeradata
Initial release20152001199220142005
Current release1.0.0, September 202311.0, December 20221902 (16.0.11328.20222), March 20193.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercial inforestricted free version is availablecommercial infoBundled with Microsoft OfficeOpen Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++C++Scala
Server operating systemsAll OS with a Java JDK11 or higherLinux
OS X
Windows
Windows infoNot a real database server, but making use of DLLsLinux
OS X
Windows
Linux
Data schemeyesschema-free infoSchema can be enforcedyesyesFlexible 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 dateyesyesyesyesyes
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.yesnoyes infoin Aster File Store
Secondary indexesyesyesnoyes
SQL infoSupport of SQLSQL-like query languageyes infoSQL92yes infobut not compliant to any SQL standardSQL-like DML and DDL statementsyes
APIs and other access methodsJDBCJava 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
JDBC
ODBC
ADO.NET
JDBC
ODBC
OLE DB
Supported programming languagesGo
Java
Python
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET
Java
Python
R
Scala
C
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresyes infovia XQuery or JavaScriptyes infosince Access 2010 using the ACE-enginenoR packages
Triggersyesyes infosince Access 2010 using the ACE-enginenono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningShardingnoneyes, utilizing Spark CoreSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesnonenoneyes 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 methodsyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsnoyes infoSQL Map-Reduce Framework
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infocan act as a resource manager in an XA/JTA transactionACID infobut no files for transaction loggingnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes infobut no files for transaction loggingyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes, with Range Indexesnono
User concepts infoAccess controlRole-based access control at the document and subdocument levelsno infoa simple user-level security was built in till version Access 2003nofine grained access rights according to SQL-standard

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More resources
Apache PinotMarkLogicMicrosoft AccessSpark SQLTeradata Aster
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