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DBMS > Apache Druid vs. Google Cloud Datastore vs. Spark SQL vs. Valentina Server

System Properties Comparison Apache Druid vs. Google Cloud Datastore vs. Spark SQL vs. Valentina Server

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonSpark SQL  Xexclude from comparisonValentina Server  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformSpark SQL is a component on top of 'Spark Core' for structured data processingObject-relational database and reports server
Primary database modelRelational DBMS
Time Series DBMS
Document storeRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.34
Rank#88  Overall
#48  Relational DBMS
#7  Time Series DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.17
Rank#327  Overall
#145  Relational DBMS
Websitedruid.apache.orgcloud.google.com/­datastorespark.apache.org/­sqlwww.valentina-db.net
Technical documentationdruid.apache.org/­docs/­latest/­designcloud.google.com/­datastore/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.htmlvalentina-db.com/­docs/­dokuwiki/­v5/­doku.php
DeveloperApache Software Foundation and contributorsGoogleApache Software FoundationParadigma Software
Initial release2012200820141999
Current release29.0.1, April 20243.5.0 ( 2.13), September 20235.7.5
License infoCommercial or Open SourceOpen Source infoApache license v2commercialOpen Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaScala
Server operating systemsLinux
OS X
Unix
hostedLinux
OS X
Windows
Linux
OS X
Windows
Data schemeyes infoschema-less columns are supportedschema-freeyesyes
Typing infopredefined data types such as float or dateyesyes, details hereyesyes
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.nonono
Secondary indexesyesyesnoyes
SQL infoSupport of SQLSQL for queryingSQL-like query language (GQL)SQL-like DML and DDL statementsyes
APIs and other access methodsJDBC
RESTful HTTP/JSON API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
ODBC
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Python
R
Scala
.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET
Server-side scripts infoStored proceduresnousing Google App Enginenoyes
TriggersnoCallbacks using the Google Apps Enginenoyes
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesMulti-source replication using Paxosnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflowno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
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 systemAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)nofine grained access rights according to SQL-standard

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More resources
Apache DruidGoogle Cloud DatastoreSpark SQLValentina Server
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