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DBMS > Databricks vs. Firebase Realtime Database vs. Google Cloud Datastore vs. Hawkular Metrics vs. STSdb

System Properties Comparison Databricks vs. Firebase Realtime Database vs. Google Cloud Datastore vs. Hawkular Metrics vs. STSdb

Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonFirebase Realtime Database  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonSTSdb  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-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformHawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.Key-Value Store with special method for indexing infooptimized for high performance using a special indexing method
Primary database modelDocument store
Relational DBMS
Document storeDocument storeTime Series DBMSKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score14.29
Rank#39  Overall
#6  Document stores
Score4.47
Rank#76  Overall
#12  Document stores
Score0.00
Rank#379  Overall
#40  Time Series DBMS
Score0.04
Rank#360  Overall
#52  Key-value stores
Websitewww.databricks.comfirebase.google.com/­products/­realtime-databasecloud.google.com/­datastorewww.hawkular.orggithub.com/­STSSoft/­STSdb4
Technical documentationdocs.databricks.comfirebase.google.com/­docs/­databasecloud.google.com/­datastore/­docswww.hawkular.org/­hawkular-metrics/­docs/­user-guide
DeveloperDatabricksGoogle infoacquired by Google 2014GoogleCommunity supported by Red HatSTS Soft SC
Initial release20132012200820142011
Current release4.0.8, September 2015
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infoApache 2.0Open Source infoGPLv2, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC#
Server operating systemshostedhostedhostedLinux
OS X
Windows
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesyes, details hereyesyes infoprimitive types and user defined types (classes)
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 indexesyesyesyesnono
SQL infoSupport of SQLwith Databricks SQLnoSQL-like query language (GQL)nono
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
Android
iOS
JavaScript API
RESTful HTTP API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
HTTP REST.NET Client API
Supported programming languagesPython
R
Scala
Java
JavaScript
Objective-C
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Go
Java
Python
Ruby
C#
Java
Server-side scripts infoStored proceduresuser defined functions and aggregateslimited functionality with using 'rules'using Google App Enginenono
TriggersCallbacks are triggered when data changesCallbacks using the Google Apps Engineyes infovia Hawkular Alertingno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infobased on Cassandranone
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication using Paxosselectable replication factor infobased on Cassandranone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflownono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
Immediate 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.Eventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDyesACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlyes, based on authentication and database rulesAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)nono
More information provided by the system vendor
DatabricksFirebase Realtime DatabaseGoogle Cloud DatastoreHawkular MetricsSTSdb
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksFirebase Realtime DatabaseGoogle Cloud DatastoreHawkular MetricsSTSdb
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