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DBMS > Apache Kylin vs. Databricks vs. Geode vs. HarperDB vs. Kinetica

System Properties Comparison Apache Kylin vs. Databricks vs. Geode vs. HarperDB vs. Kinetica

Editorial information provided by DB-Engines
NameApache Kylin  Xexclude from comparisonDatabricks  Xexclude from comparisonGeode  Xexclude from comparisonHarperDB  Xexclude from comparisonKinetica  Xexclude from comparison
DescriptionA distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stackThe 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.Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processesUltra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.Fully vectorized database across both GPUs and CPUs
Primary database modelRelational DBMSDocument store
Relational DBMS
Key-value storeDocument storeRelational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#172  Overall
#79  Relational DBMS
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score1.92
Rank#131  Overall
#23  Key-value stores
Score0.55
Rank#248  Overall
#38  Document stores
Score0.64
Rank#236  Overall
#109  Relational DBMS
Websitekylin.apache.orgwww.databricks.comgeode.apache.orgwww.harperdb.iowww.kinetica.com
Technical documentationkylin.apache.org/­docsdocs.databricks.comgeode.apache.org/­docsdocs.harperdb.io/­docsdocs.kinetica.com
DeveloperApache Software Foundation, originally contributed from eBay IncDatabricksOriginally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.HarperDBKinetica
Initial release20152013200220172012
Current release3.1.0, July 20201.1, February 20173.1, August 20217.1, August 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache Version 2; commercial licenses available as Gemfirecommercial infofree community edition availablecommercial
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaNode.jsC, C++
Server operating systemsLinuxhostedAll OS with a Java VM infothe JDK (8 or later) is also requiredLinux
OS X
Linux
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)schema-freedynamic schemayes
Typing infopredefined data types such as float or dateyesyesyes infoJSON data typesyes
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.noyesnonono
Secondary indexesyesyesnoyesyes
SQL infoSupport of SQLANSI SQL for queries (using Apache Calcite)with Databricks SQLSQL-like query language (OQL)SQL-like data manipulation statementsSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
Java Client API
Memcached protocol
RESTful HTTP API
JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket
JDBC
ODBC
RESTful HTTP API
Supported programming languagesPython
R
Scala
.Net
All JVM based languages
C++
Groovy
Java
Scala
.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift
C++
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresuser defined functions and aggregatesuser defined functionsCustom Functions infosince release 3.1user defined functions
Triggersyes infoCache Event Listenersnoyes infotriggers when inserted values for one or more columns fall within a specified range
Partitioning methods infoMethods for storing different data on different nodesShardingA table resides as a whole on one (or more) nodes in a clusterSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replicationyes infothe nodes on which a table resides can be definedSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDyes, on a single nodeAtomic execution of specific operationsno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes, using LMDByes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesyesyes infoGPU vRAM or System RAM
User concepts infoAccess controlAccess rights per client and object definableAccess rights for users and rolesAccess rights for users and roles on table level
More information provided by the system vendor
Apache KylinDatabricksGeodeHarperDBKinetica
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
Apache KylinDatabricksGeodeHarperDBKinetica
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