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DBMS > Apache Phoenix vs. CrateDB vs. Databricks

System Properties Comparison Apache Phoenix vs. CrateDB vs. Databricks

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonCrateDB  Xexclude from comparisonDatabricks  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseDistributed 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.
Primary database modelRelational DBMSDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Document store
Relational DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.06
Rank#123  Overall
#58  Relational DBMS
Score0.71
Rank#227  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#9  Vector DBMS
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Websitephoenix.apache.orgcratedb.comwww.databricks.com
Technical documentationphoenix.apache.orgcratedb.com/­docsdocs.databricks.com
DeveloperApache Software FoundationCrateDatabricks
Initial release201420132013
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 2019
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Sourcecommercial
Cloud-based only infoOnly available as a cloud servicenonoyes
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
Server operating systemsLinux
Unix
Windows
All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supporthosted
Data schemeyes infolate-bound, schema-on-read capabilitiesFlexible Schema (defined schema, partial schema, schema free)Flexible Schema (defined schema, partial schema, schema free)
Typing infopredefined data types such as float or dateyesyes
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.nonoyes
Secondary indexesyesyesyes
SQL infoSupport of SQLyesyes, but no triggers and constraints, and PostgreSQL compatibilitywith Databricks SQL
APIs and other access methodsJDBCADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
.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
Server-side scripts infoStored proceduresuser defined functionsuser defined functions (Javascript)user defined functions and aggregates
Triggersnono
Partitioning methods infoMethods for storing different data on different nodesShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Configurable replication on table/partition-levelyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyEventual Consistency
Read-after-write consistency on record level
Immediate Consistency
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDno infounique row identifiers can be used for implementing an optimistic concurrency control strategyACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnono
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyrights management via user accounts
More information provided by the system vendor
Apache PhoenixCrateDBDatabricks
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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More resources
Apache PhoenixCrateDBDatabricks
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