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DBMS > Amazon Neptune vs. Databricks vs. MarkLogic vs. Riak KV

System Properties Comparison Amazon Neptune vs. Databricks vs. MarkLogic vs. Riak KV

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Editorial information provided by DB-Engines
NameAmazon Neptune  Xexclude from comparisonDatabricks  Xexclude from comparisonMarkLogic  Xexclude from comparisonRiak KV  Xexclude from comparison
DescriptionFast, reliable graph database built for the cloudThe 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.Operational and transactional Enterprise NoSQL databaseDistributed, fault tolerant key-value store
Primary database modelGraph DBMS
RDF store
Document store
Relational DBMS
Document store
Native XML DBMS
RDF store infoas of version 7
Search engine
Key-value store infowith links between data sets and object tags for the creation of secondary indexes
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.29
Rank#113  Overall
#9  Graph DBMS
#5  RDF stores
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score5.18
Rank#63  Overall
#11  Document stores
#1  Native XML DBMS
#1  RDF stores
#7  Search engines
Score4.01
Rank#79  Overall
#9  Key-value stores
Websiteaws.amazon.com/­neptunewww.databricks.comwww.progress.com/­marklogic
Technical documentationaws.amazon.com/­neptune/­developer-resourcesdocs.databricks.comwww.progress.com/­marklogic/­documentationwww.tiot.jp/­riak-docs/­riak/­kv/­latest
DeveloperAmazonDatabricksMarkLogic Corp.OpenSource, formerly Basho Technologies
Initial release2017201320012009
Current release11.0, December 20223.2.0, December 2022
License infoCommercial or Open Sourcecommercialcommercialcommercial inforestricted free version is availableOpen Source infoApache version 2, commercial enterprise edition
Cloud-based only infoOnly available as a cloud serviceyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++Erlang
Server operating systemshostedhostedLinux
OS X
Windows
Linux
OS X
Data schemeschema-freeFlexible Schema (defined schema, partial schema, schema free)schema-free infoSchema can be enforcedschema-free
Typing infopredefined data types such as float or dateyesyesno
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.noyesyesno
Secondary indexesnoyesyesrestricted
SQL infoSupport of SQLnowith Databricks SQLyes infoSQL92no
APIs and other access methodsOpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin
JDBC
ODBC
RESTful HTTP API
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
HTTP API
Native Erlang Interface
Supported programming languagesC#
Go
Java
JavaScript
PHP
Python
Ruby
Scala
Python
R
Scala
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
Server-side scripts infoStored proceduresnouser defined functions and aggregatesyes infovia XQuery or JavaScriptErlang
Triggersnoyesyes infopre-commit hooks and post-commit hooks
Partitioning methods infoMethods for storing different data on different nodesnoneShardingSharding infono "single point of failure"
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.yesyesselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integrityyes infoRelationships in graphsnono infolinks between data sets can be stored
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACID infocan act as a resource manager in an XA/JTA transactionno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyes infowith encyption-at-restyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes, with Range Indexes
User concepts infoAccess controlAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)Role-based access control at the document and subdocument levelsyes, using Riak Security
More information provided by the system vendor
Amazon NeptuneDatabricksMarkLogicRiak KV
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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Amazon NeptuneDatabricksMarkLogicRiak KV
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