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DBMS > CrateDB vs. Databricks vs. Hive vs. Linter

System Properties Comparison CrateDB vs. Databricks vs. Hive vs. Linter

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Editorial information provided by DB-Engines
NameCrateDB  Xexclude from comparisonDatabricks  Xexclude from comparisonHive  Xexclude from comparisonLinter  Xexclude from comparison
DescriptionDistributed Database based on LuceneThe 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 HadoopRDBMS for high security requirements
Primary database modelDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Document store
Relational DBMS
Relational DBMSRelational DBMS
Secondary database modelsRelational DBMSSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.73
Rank#224  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score61.17
Rank#18  Overall
#12  Relational DBMS
Score0.09
Rank#346  Overall
#152  Relational DBMS
Websitecratedb.comwww.databricks.comhive.apache.orglinter.ru
Technical documentationcratedb.com/­docsdocs.databricks.comcwiki.apache.org/­confluence/­display/­Hive/­Home
DeveloperCrateDatabricksApache Software Foundation infoinitially developed by Facebookrelex.ru
Initial release2013201320121990
Current release3.1.3, April 2022
License infoCommercial or Open SourceOpen SourcecommercialOpen Source infoApache Version 2commercial
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.
Implementation languageJavaJavaC and C++
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supporthostedAll OS with a Java VMAIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)Flexible Schema (defined schema, partial schema, schema free)yesyes
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.noyesno
Secondary indexesyesyesyesyes
SQL infoSupport of SQLyes, but no triggers and constraints, and PostgreSQL compatibilitywith Databricks SQLSQL-like DML and DDL statementsyes
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
JDBC
ODBC
Thrift
ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)
Supported programming languages.NET
Erlang
Go infocommunity maintained client
Java
JavaScript (Node.js) infocommunity maintained client
Perl infocommunity maintained client
PHP
Python
R
Ruby infocommunity maintained client
Scala infocommunity maintained client
Python
R
Scala
C++
Java
PHP
Python
C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl
Server-side scripts infoStored proceduresuser defined functions (Javascript)user defined functions and aggregatesyes infouser defined functions and integration of map-reduceyes infoproprietary syntax with the possibility to convert from PL/SQL
Triggersnonoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-levelyesselectable replication factorSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infoquery execution via MapReduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infounique row identifiers can be used for implementing an optimistic concurrency control strategyACIDnoACID
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 controlrights management via user accountsAccess rights for users, groups and rolesfine grained access rights according to SQL-standard
More information provided by the system vendor
CrateDBDatabricksHiveLinter
Specific characteristicsThe enterprise database for time series, documents, and vectors. Distributed - Native...
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Supported database models : In addition to the Document store and Relational DBMS...
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Competitive advantagesResponse time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
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Typical application scenarios​ IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
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Key customersAcross all continents, CrateDB is used by companies of all sizes to meet the most...
» more
Market metricsThe CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
» more
Licensing and pricing modelsSee CrateDB pricing >
» more

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CrateDBDatabricksHiveLinter
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