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DBMS > Databricks vs. FatDB vs. Google Cloud Datastore vs. Sphinx vs. Warp 10

System Properties Comparison Databricks vs. FatDB vs. Google Cloud Datastore vs. Sphinx vs. Warp 10

Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonFatDB  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonSphinx  Xexclude from comparisonWarp 10  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
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.A .NET NoSQL DBMS that can integrate with and extend SQL Server.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformOpen source search engine for searching in data from different sources, e.g. relational databasesTimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase
Primary database modelDocument store
Relational DBMS
Document store
Key-value store
Document storeSearch engineTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score4.36
Rank#72  Overall
#12  Document stores
Score5.95
Rank#55  Overall
#5  Search engines
Score0.14
Rank#344  Overall
#32  Time Series DBMS
Websitewww.databricks.comcloud.google.com/­datastoresphinxsearch.comwww.warp10.io
Technical documentationdocs.databricks.comcloud.google.com/­datastore/­docssphinxsearch.com/­docswww.warp10.io/­content/­02_Getting_started
DeveloperDatabricksFatCloudGoogleSphinx Technologies Inc.SenX
Initial release20132012200820012015
Current release3.5.1, February 2023
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infoGPL version 2, commercial licence availableOpen Source infoApache License 2.0
Cloud-based only infoOnly available as a cloud serviceyesnoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC#C++Java
Server operating systemshostedWindowshostedFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesyes, details herenoyes
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.yesnono
Secondary indexesyesyesyesyes infofull-text index on all search fieldsno
SQL infoSupport of SQLwith Databricks SQLno infoVia inetgration in SQL ServerSQL-like query language (GQL)SQL-like query language (SphinxQL)no
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Proprietary protocolHTTP API
Jupyter
WebSocket
Supported programming languagesPython
R
Scala
C#.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresuser defined functions and aggregatesyes infovia applicationsusing Google App Enginenoyes infoWarpScript
Triggersyes infovia applicationsCallbacks using the Google Apps Enginenono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoPartitioning is done manually, search queries against distributed index is supportedSharding infobased on HBase
Replication methods infoMethods for redundantly storing data on multiple nodesyesselectable replication factorMulti-source replication using Paxosnoneselectable replication factor infobased on HBase
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyes infousing Google Cloud Dataflownono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
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.Immediate Consistency infobased on HBase
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes
User concepts infoAccess controlno infoCan implement custom security layer via applicationsAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)noMandatory use of cryptographic tokens, containing fine-grained authorizations
More information provided by the system vendor
DatabricksFatDBGoogle Cloud DatastoreSphinxWarp 10
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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DatabricksFatDBGoogle Cloud DatastoreSphinxWarp 10
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