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DBMS > Apache Druid vs. CrateDB vs. Databricks vs. HBase

System Properties Comparison Apache Druid vs. CrateDB vs. Databricks vs. HBase

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonCrateDB  Xexclude from comparisonDatabricks  Xexclude from comparisonHBase  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataDistributed 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.Wide-column store based on Apache Hadoop and on concepts of BigTable
Primary database modelRelational DBMS
Time Series DBMS
Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Document store
Relational DBMS
Wide column store
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.25
Rank#90  Overall
#47  Relational DBMS
#7  Time Series DBMS
Score0.71
Rank#227  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#10  Vector DBMS
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score27.97
Rank#26  Overall
#2  Wide column stores
Websitedruid.apache.orgcratedb.comwww.databricks.comhbase.apache.org
Technical documentationdruid.apache.org/­docs/­latest/­designcratedb.com/­docsdocs.databricks.comhbase.apache.org/­book.html
DeveloperApache Software Foundation and contributorsCrateDatabricksApache Software Foundation infoApache top-level project, originally developed by Powerset
Initial release2012201320132008
Current release29.0.1, April 20242.3.4, January 2021
License infoCommercial or Open SourceOpen Source infoApache license v2Open SourcecommercialOpen Source infoApache version 2
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaJava
Server operating systemsLinux
OS X
Unix
All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supporthostedLinux
Unix
Windows infousing Cygwin
Data schemeyes infoschema-less columns are supportedFlexible Schema (defined schema, partial schema, schema free)Flexible Schema (defined schema, partial schema, schema free)schema-free, schema definition possible
Typing infopredefined data types such as float or dateyesyesoptions to bring your own types, AVRO
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.nonoyesno
Secondary indexesyesyesyesno
SQL infoSupport of SQLSQL for queryingyes, but no triggers and constraints, and PostgreSQL compatibilitywith Databricks SQLno
APIs and other access methodsJDBC
RESTful HTTP/JSON API
ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
Java API
RESTful HTTP API
Thrift
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
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
C
C#
C++
Groovy
Java
PHP
Python
Scala
Server-side scripts infoStored proceduresnouser defined functions (Javascript)user defined functions and aggregatesyes infoCoprocessors in Java
Triggersnonoyes
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesConfigurable replication on table/partition-levelyesMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyImmediate Consistency or Eventual Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono infounique row identifiers can be used for implementing an optimistic concurrency control strategyACIDSingle row ACID (across millions of columns)
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.nononoyes
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemrights management via user accountsAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC
More information provided by the system vendor
Apache DruidCrateDBDatabricksHBase
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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Apache DruidCrateDBDatabricksHBase
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