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DBMS > CrateDB vs. Databricks vs. GeoSpock vs. Lovefield

System Properties Comparison CrateDB vs. Databricks vs. GeoSpock vs. Lovefield

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Editorial information provided by DB-Engines
NameCrateDB  Xexclude from comparisonDatabricks  Xexclude from comparisonGeoSpock  Xexclude from comparisonLovefield  Xexclude from comparison
GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.
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.Spatial and temporal data processing engine for extreme data scaleEmbeddable relational database for web apps written in pure JavaScript
Primary database modelDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Document store
Relational DBMS
Relational DBMSRelational DBMS
Secondary database modelsRelational DBMSTime Series 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
Score0.29
Rank#293  Overall
#133  Relational DBMS
Websitecratedb.comwww.databricks.comgeospock.comgoogle.github.io/­lovefield
Technical documentationcratedb.com/­docsdocs.databricks.comgithub.com/­google/­lovefield/­blob/­master/­docs/­spec_index.md
DeveloperCrateDatabricksGeoSpockGoogle
Initial release201320132014
Current release2.0, September 20192.1.12, February 2017
License infoCommercial or Open SourceOpen SourcecommercialcommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenoyesyesno
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 languageJavaJava, JavascriptJavaScript
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supporthostedhostedserver-less, requires a JavaScript environment (browser, Node.js) infotested with Chrome, Firefox, IE, Safari
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.noyesnono
Secondary indexesyesyestemporal, categoricalyes
SQL infoSupport of SQLyes, but no triggers and constraints, and PostgreSQL compatibilitywith Databricks SQLANSI SQL for query only (using Presto)SQL-like query language infovia JavaScript builder pattern
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
JDBC
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
JavaScript
Server-side scripts infoStored proceduresuser defined functions (Javascript)user defined functions and aggregatesnono
TriggersnonoUsing read-only observers
Partitioning methods infoMethods for storing different data on different nodesShardingAutomatic shardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-levelyesnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Immediate 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, by using IndexedDB or the cloud service Firebase Realtime Database
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nononoyes infousing MemoryDB
User concepts infoAccess controlrights management via user accountsAccess rights for users can be defined per tableno
More information provided by the system vendor
CrateDBDatabricksGeoSpockLovefield
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...
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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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CrateDBDatabricksGeoSpockLovefield
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