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DBMS > Apache Phoenix vs. Google Cloud Datastore vs. Spark SQL vs. WakandaDB

System Properties Comparison Apache Phoenix vs. Google Cloud Datastore vs. Spark SQL vs. WakandaDB

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonSpark SQL  Xexclude from comparisonWakandaDB  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformSpark SQL is a component on top of 'Spark Core' for structured data processingWakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data
Primary database modelRelational DBMSDocument storeRelational DBMSObject oriented DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.06
Rank#123  Overall
#58  Relational DBMS
Score4.36
Rank#72  Overall
#12  Document stores
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.10
Rank#356  Overall
#16  Object oriented DBMS
Websitephoenix.apache.orgcloud.google.com/­datastorespark.apache.org/­sqlwakanda.github.io
Technical documentationphoenix.apache.orgcloud.google.com/­datastore/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.htmlwakanda.github.io/­doc
DeveloperApache Software FoundationGoogleApache Software FoundationWakanda SAS
Initial release2014200820142012
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20193.5.0 ( 2.13), September 20232.7.0 (April 29, 2019), April 2019
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache 2.0Open Source infoAGPLv3, extended commercial license available
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaScalaC++, JavaScript
Server operating systemsLinux
Unix
Windows
hostedLinux
OS X
Windows
Linux
OS X
Windows
Data schemeyes infolate-bound, schema-on-read capabilitiesschema-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.nononono
Secondary indexesyesyesno
SQL infoSupport of SQLyesSQL-like query language (GQL)SQL-like DML and DDL statementsno
APIs and other access methodsJDBCgRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Python
R
Scala
JavaScript
Server-side scripts infoStored proceduresuser defined functionsusing Google App Enginenoyes
TriggersnoCallbacks using the Google Apps Enginenoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Multi-source replication using Paxosnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationyes infousing Google Cloud Dataflowno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual 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.Immediate Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnoACID
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.yesnonono
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)noyes

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More resources
Apache PhoenixGoogle Cloud DatastoreSpark SQLWakandaDB
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