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DBMS > Apache Phoenix vs. Datomic vs. Google Cloud Bigtable vs. Sqrrl

System Properties Comparison Apache Phoenix vs. Datomic vs. Google Cloud Bigtable vs. Sqrrl

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonDatomic  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonSqrrl  Xexclude from comparison
Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Adaptable, secure NoSQL built on Apache Accumulo
Primary database modelRelational DBMSRelational DBMSKey-value store
Wide column store
Document store
Graph DBMS
Key-value store
Wide column store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.06
Rank#123  Overall
#58  Relational DBMS
Score1.66
Rank#144  Overall
#66  Relational DBMS
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Websitephoenix.apache.orgwww.datomic.comcloud.google.com/­bigtablesqrrl.com
Technical documentationphoenix.apache.orgdocs.datomic.comcloud.google.com/­bigtable/­docs
DeveloperApache Software FoundationCognitectGoogleAmazon infooriginally Sqrrl Data, Inc.
Initial release2014201220152012
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20191.0.7075, December 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercial infolimited edition freecommercialcommercial
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJava, ClojureJava
Server operating systemsLinux
Unix
Windows
All OS with a Java VMhostedLinux
Data schemeyes infolate-bound, schema-on-read capabilitiesyesschema-freeschema-free
Typing infopredefined data types such as float or dateyesyesnoyes
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 SQLyesnonono
APIs and other access methodsJDBCRESTful HTTP APIgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
Clojure
Java
C#
C++
Go
Java
JavaScript (Node.js)
Python
Actionscript
C infousing GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk
Server-side scripts infoStored proceduresuser defined functionsyes infoTransaction Functionsnono
TriggersnoBy using transaction functionsnono
Partitioning methods infoMethods for storing different data on different nodesShardingnone infoBut extensive use of caching in the application peersShardingSharding infomaking use of Hadoop
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
none infoBut extensive use of caching in the application peersInternal replication in Colossus, and regional replication between two clusters in different zonesselectable replication factor infomaking use of Hadoop
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationnoyesyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency infoDocument store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDAtomic single-row operationsAtomic updates per row, document, or graph entity
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes inforecommended only for testing and developmentno
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancynoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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More resources
Apache PhoenixDatomicGoogle Cloud BigtableSqrrl
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