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DBMS > Badger vs. Databricks vs. Datomic vs. Google Cloud Datastore vs. Quasardb

System Properties Comparison Badger vs. Databricks vs. Datomic vs. Google Cloud Datastore vs. Quasardb

Editorial information provided by DB-Engines
NameBadger  Xexclude from comparisonDatabricks  Xexclude from comparisonDatomic  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonQuasardb  Xexclude from comparison
DescriptionAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.The 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.Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformDistributed, high-performance timeseries database
Primary database modelKey-value storeDocument store
Relational DBMS
Relational DBMSDocument storeTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.14
Rank#331  Overall
#49  Key-value stores
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score1.59
Rank#150  Overall
#69  Relational DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Score0.14
Rank#332  Overall
#29  Time Series DBMS
Websitegithub.com/­dgraph-io/­badgerwww.databricks.comwww.datomic.comcloud.google.com/­datastorequasar.ai
Technical documentationgodoc.org/­github.com/­dgraph-io/­badgerdocs.databricks.comdocs.datomic.comcloud.google.com/­datastore/­docsdoc.quasar.ai/­master
DeveloperDGraph LabsDatabricksCognitectGooglequasardb
Initial release20172013201220082009
Current release1.0.6735, June 20233.14.1, January 2024
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialcommercial infolimited edition freecommercialcommercial infoFree community edition, Non-profit organizations and non-commercial usage are eligible for free licenses
Cloud-based only infoOnly available as a cloud servicenoyesnoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoJava, ClojureC++
Server operating systemsBSD
Linux
OS X
Solaris
Windows
hostedAll OS with a Java VMhostedBSD
Linux
OS X
Windows
Data schemeschema-freeFlexible Schema (defined schema, partial schema, schema free)yesschema-freeschema-free
Typing infopredefined data types such as float or datenoyesyes, details hereyes infointeger and binary
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.noyesnonono
Secondary indexesnoyesyesyesyes infowith tags
SQL infoSupport of SQLnowith Databricks SQLnoSQL-like query language (GQL)SQL-like query language
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
RESTful HTTP APIgRPC (using protocol buffers) API
RESTful HTTP/JSON API
HTTP API
Supported programming languagesGoPython
R
Scala
Clojure
Java
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R
Server-side scripts infoStored proceduresnouser defined functions and aggregatesyes infoTransaction Functionsusing Google App Engineno
TriggersnoBy using transaction functionsCallbacks using the Google Apps Engineno
Partitioning methods infoMethods for storing different data on different nodesnonenone infoBut extensive use of caching in the application peersShardingSharding infoconsistent hashing
Replication methods infoMethods for redundantly storing data on multiple nodesnoneyesnone infoBut extensive use of caching in the application peersMulti-source replication using PaxosSource-replica replication with selectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes infousing Google Cloud Dataflowwith Hadoop integration
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate ConsistencyImmediate 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 integritynonoyes infovia ReferenceProperties or Ancestor pathsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyes infoby using LevelDB
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes inforecommended only for testing and developmentnoyes infoTransient mode
User concepts infoAccess controlnonoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Cryptographically strong user authentication and audit trail
More information provided by the system vendor
BadgerDatabricksDatomicGoogle Cloud DatastoreQuasardb
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
BadgerDatabricksDatomicGoogle Cloud DatastoreQuasardb
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