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DBMS > Databricks vs. Drizzle vs. Kinetica vs. Microsoft Azure Table Storage

System Properties Comparison Databricks vs. Drizzle vs. Kinetica vs. Microsoft Azure Table Storage

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonDrizzle  Xexclude from comparisonKinetica  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.
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.MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Fully vectorized database across both GPUs and CPUsA Wide Column Store for rapid development using massive semi-structured datasets
Primary database modelDocument store
Relational DBMS
Relational DBMSRelational DBMSWide column store
Secondary database modelsSpatial 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
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score4.04
Rank#77  Overall
#6  Wide column stores
Websitewww.databricks.comwww.kinetica.comazure.microsoft.com/­en-us/­services/­storage/­tables
Technical documentationdocs.databricks.comdocs.kinetica.com
DeveloperDatabricksDrizzle project, originally started by Brian AkerKineticaMicrosoft
Initial release2013200820122012
Current release7.2.4, September 20127.1, August 2021
License infoCommercial or Open SourcecommercialOpen Source infoGNU GPLcommercialcommercial
Cloud-based only infoOnly available as a cloud serviceyesnonoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C, C++
Server operating systemshostedFreeBSD
Linux
OS X
Linuxhosted
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesyesschema-free
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.yesnono
Secondary indexesyesyesyesno
SQL infoSupport of SQLwith Databricks SQLyes infowith proprietary extensionsSQL-like DML and DDL statementsno
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBCJDBC
ODBC
RESTful HTTP API
RESTful HTTP API
Supported programming languagesPython
R
Scala
C
C++
Java
PHP
C++
Java
JavaScript (Node.js)
Python
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresuser defined functions and aggregatesnouser defined functionsno
Triggersno infohooks for callbacks inside the server can be used.yes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication
Source-replica replication
Source-replica replicationyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integrityyesyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnooptimistic locking
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.noyes infoGPU vRAM or System RAMno
User concepts infoAccess controlPluggable authentication mechanisms infoe.g. LDAP, HTTPAccess rights for users and roles on table levelAccess rights based on private key authentication or shared access signatures
More information provided by the system vendor
DatabricksDrizzleKineticaMicrosoft Azure Table Storage
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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DatabricksDrizzleKineticaMicrosoft Azure Table Storage
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