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DBMS > Databricks vs. Hive vs. Neo4j vs. TigerGraph

System Properties Comparison Databricks vs. Hive vs. Neo4j vs. TigerGraph

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonHive  Xexclude from comparisonNeo4j  Xexclude from comparisonTigerGraph  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.data warehouse software for querying and managing large distributed datasets, built on HadoopScalable, ACID-compliant graph database designed with a high-performance distributed cluster architecture, available in self-hosted and cloud offeringsA complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time
Primary database modelDocument store
Relational DBMS
Relational DBMSGraph DBMSGraph 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
Score59.76
Rank#18  Overall
#12  Relational DBMS
Score44.89
Rank#21  Overall
#1  Graph DBMS
Score1.80
Rank#138  Overall
#13  Graph DBMS
Websitewww.databricks.comhive.apache.orgneo4j.comwww.tigergraph.com
Technical documentationdocs.databricks.comcwiki.apache.org/­confluence/­display/­Hive/­Homeneo4j.com/­docsdocs.tigergraph.com
DeveloperDatabricksApache Software Foundation infoinitially developed by FacebookNeo4j, Inc.
Initial release2013201220072017
Current release3.1.3, April 20225.20, May 2024
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2Open Source infoGPL version3, commercial licenses availablecommercial
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Neo4j Aura: Neo4j’s fully managed cloud service: The zero-admin, always-on graph database for cloud developers.
Implementation languageJavaJava, ScalaC++
Server operating systemshostedAll OS with a Java VMLinux infoCan also be used server-less as embedded Java database.
OS X
Solaris
Windows
Linux
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesschema-free and schema-optionalyes
Typing infopredefined data types such as float or dateyesyesyes
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.yesno
Secondary indexesyesyesyes infopluggable indexing subsystem, by default Apache Lucene
SQL infoSupport of SQLwith Databricks SQLSQL-like DML and DDL statementsnoSQL-like query language (GSQL)
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
Thrift
Bolt protocol
Cypher query language
Java API
Neo4j-OGM infoObject Graph Mapper
RESTful HTTP API
Spring Data Neo4j
TinkerPop 3
GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API
Supported programming languagesPython
R
Scala
C++
Java
PHP
Python
.Net
Clojure
Elixir
Go
Groovy
Haskell
Java
JavaScript
Perl
PHP
Python
Ruby
Scala
C++
Java
Server-side scripts infoStored proceduresuser defined functions and aggregatesyes infouser defined functions and integration of map-reduceyes infoUser defined Procedures and Functionsyes
Triggersnoyes infovia event handlerno
Partitioning methods infoMethods for storing different data on different nodesShardingyes using Neo4j Fabric
Replication methods infoMethods for redundantly storing data on multiple nodesyesselectable replication factorCausal Clustering using Raft protocol infoavailable in in Enterprise Version only
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyCausal and Eventual Consistency configurable in Causal Cluster setup
Immediate Consistency in stand-alone mode
Foreign keys infoReferential integritynoyes infoRelationships in graphsyes infoRelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDACID
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.nono
User concepts infoAccess controlAccess rights for users, groups and rolesUsers, roles and permissions. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)Role-based access control
More information provided by the system vendor
DatabricksHiveNeo4jTigerGraph
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
» more
Neo4j delivers graph technology that has been battle tested for performance and scale...
» more
Competitive advantagesNeo4j is the market leader, graph database category creator, and the most widely...
» more
Typical application scenariosReal-Time Recommendations Master Data Management Identity and Access Management Network...
» more
Key customersOver 800 commercial customers and over 4300 startups use Neo4j. Flagship customers...
» more
Market metricsNeo4j boasts the world's largest graph database ecosystem with more than 140 million...
» more
Licensing and pricing modelsGPL v3 license that can be used all the places where you might use MySQL. Neo4j Commercial...
» more
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