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DBMS > Databricks vs. EDB Postgres vs. Hazelcast vs. Kinetica

System Properties Comparison Databricks vs. EDB Postgres vs. Hazelcast vs. Kinetica

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonEDB Postgres  Xexclude from comparisonHazelcast  Xexclude from comparisonKinetica  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.The EDB Postgres Platform is an enterprise-class data management platform based on the open source database PostgreSQL with flexible deployment options and Oracle compatibility features, complemented by tool kits for management, integration, and migration.A widely adopted in-memory data gridFully vectorized database across both GPUs and CPUs
Primary database modelDocument store
Relational DBMS
Relational DBMSKey-value storeRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
Document store infoJSON support with IMDG 3.12Spatial DBMS
Time Series 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
Score1.91
Rank#130  Overall
#60  Relational DBMS
Score5.46
Rank#61  Overall
#7  Key-value stores
Score0.66
Rank#234  Overall
#107  Relational DBMS
Websitewww.databricks.comwww.enterprisedb.comhazelcast.comwww.kinetica.com
Technical documentationdocs.databricks.comwww.enterprisedb.com/­docshazelcast.org/­imdg/­docsdocs.kinetica.com
DeveloperDatabricksEnterpriseDBHazelcastKinetica
Initial release2013200520082012
Current release14, December 20215.3.6, November 20237.1, August 2021
License infoCommercial or Open Sourcecommercialcommercial infoBSD for PostgreSQL-componentsOpen Source infoApache Version 2; commercial licenses availablecommercial
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCJavaC, C++
Server operating systemshostedLinux
Windows
All OS with a Java VMLinux
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesschema-freeyes
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.yesyes infospecific XML-type available, but no XML query functionality.yes infothe object must implement a serialization strategyno
Secondary indexesyesyesyesyes
SQL infoSupport of SQLwith Databricks SQLyes infostandard with numerous extensionsSQL-like query languageSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JCache
JPA
Memcached protocol
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
Supported programming languagesPython
R
Scala
.Net
C
C++
Delphi
Java
Perl
PHP
Python
Tcl
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C++
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresuser defined functions and aggregatesuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.yes infoEvent Listeners, Executor Servicesuser defined functions
Triggersyesyes infoEventsyes infotriggers when inserted values for one or more columns fall within a specified range
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning infoby hash, list or rangeShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replicationyes infoReplicated MapSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDone or two-phase-commit; repeatable reads; read commitedno
Concurrency infoSupport for concurrent manipulation of datayesyes, multi-version concurrency control (MVCC)yesyes
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.nonoyesyes infoGPU vRAM or System RAM
User concepts infoAccess controlfine grained access rights according to SQL-standardRole-based access controlAccess rights for users and roles on table level
More information provided by the system vendor
DatabricksEDB PostgresHazelcastKinetica
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksEDB PostgresHazelcastKinetica
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