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DBMS > Apache Druid vs. Atos Standard Common Repository vs. Spark SQL

System Properties Comparison Apache Druid vs. Atos Standard Common Repository vs. Spark SQL

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonAtos Standard Common Repository  Xexclude from comparisonSpark SQL  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMS
Time Series DBMS
Document store
Key-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.29
Rank#95  Overall
#49  Relational DBMS
#7  Time Series DBMS
Score19.15
Rank#33  Overall
#20  Relational DBMS
Websitedruid.apache.orgatos.net/en/convergence-creators/portfolio/standard-common-repositoryspark.apache.org/­sql
Technical documentationdruid.apache.org/­docs/­latest/­designspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software Foundation and contributorsAtos Convergence CreatorsApache Software Foundation
Initial release201220162014
Current release29.0.1, April 202417033.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache license v2commercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaScala
Server operating systemsLinux
OS X
Unix
LinuxLinux
OS X
Windows
Data schemeyes infoschema-less columns are supportedSchema and schema-less with LDAP viewsyes
Typing infopredefined data types such as float or dateyesoptionalyes
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 indexesyesyesno
SQL infoSupport of SQLSQL for queryingnoSQL-like DML and DDL statements
APIs and other access methodsJDBC
RESTful HTTP/JSON API
LDAPJDBC
ODBC
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
All languages with LDAP bindingsJava
Python
R
Scala
Server-side scripts infoStored proceduresnonono
Triggersnoyesno
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedSharding infocell divisionyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesyesnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoAtomic execution of specific operationsno
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.noyesno
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemLDAP bind authenticationno

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More resources
Apache DruidAtos Standard Common RepositorySpark SQL
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