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DBMS > Databricks vs. Google Cloud Firestore vs. HBase vs. Riak KV

System Properties Comparison Databricks vs. Google Cloud Firestore vs. HBase vs. Riak KV

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonGoogle Cloud Firestore  Xexclude from comparisonHBase  Xexclude from comparisonRiak KV  Xexclude from comparison
DescriptionThe 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.Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.Wide-column store based on Apache Hadoop and on concepts of BigTableDistributed, fault tolerant key-value store
Primary database modelDocument store
Relational DBMS
Document storeWide column storeKey-value store infowith links between data sets and object tags for the creation of secondary indexes
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score7.85
Rank#51  Overall
#8  Document stores
Score30.50
Rank#26  Overall
#2  Wide column stores
Score4.10
Rank#82  Overall
#9  Key-value stores
Websitewww.databricks.comfirebase.google.com/­products/­firestorehbase.apache.org
Technical documentationdocs.databricks.comfirebase.google.com/­docs/­firestorehbase.apache.org/­book.htmlwww.tiot.jp/­riak-docs/­riak/­kv/­latest
DeveloperDatabricksGoogleApache Software Foundation infoApache top-level project, originally developed by PowersetOpenSource, formerly Basho Technologies
Initial release2013201720082009
Current release2.3.4, January 20213.2.0, December 2022
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache version 2Open Source infoApache version 2, commercial enterprise edition
Cloud-based only infoOnly available as a cloud serviceyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaErlang
Server operating systemshostedhostedLinux
Unix
Windows infousing Cygwin
Linux
OS X
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeschema-free, schema definition possibleschema-free
Typing infopredefined data types such as float or dateyesoptions to bring your own types, AVROno
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.yesnonono
Secondary indexesyesyesnorestricted
SQL infoSupport of SQLwith Databricks SQLnonono
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API
Java API
RESTful HTTP API
Thrift
HTTP API
Native Erlang Interface
Supported programming languagesPython
R
Scala
Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python
C
C#
C++
Groovy
Java
PHP
Python
Scala
C infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
Server-side scripts infoStored proceduresuser defined functions and aggregatesyes, Firebase Rules & Cloud Functionsyes infoCoprocessors in JavaErlang
Triggersyes, with Cloud Functionsyesyes infopre-commit hooks and post-commit hooks
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infono "single point of failure"
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replicationMulti-source replication
Source-replica replication
selectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsUsing Cloud Dataflowyesyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual ConsistencyEventual Consistency
Foreign keys infoReferential integritynonono infolinks between data sets can be stored
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDyesSingle row ACID (across millions of columns)no
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.noyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACyes, using Riak Security
More information provided by the system vendor
DatabricksGoogle Cloud FirestoreHBaseRiak KV
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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DatabricksGoogle Cloud FirestoreHBaseRiak KV
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