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DBMS > Databricks vs. Google Cloud Datastore vs. mSQL vs. Oracle Berkeley DB

System Properties Comparison Databricks vs. Google Cloud Datastore vs. mSQL vs. Oracle Berkeley DB

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonmSQL infoMini SQL  Xexclude from comparisonOracle Berkeley DB  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.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformmSQL (Mini SQL) is a simple and lightweight RDBMSWidely used in-process key-value store
Primary database modelDocument store
Relational DBMS
Document storeRelational DBMSKey-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
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
Score1.27
Rank#169  Overall
#76  Relational DBMS
Score2.01
Rank#126  Overall
#21  Key-value stores
#3  Native XML DBMS
Websitewww.databricks.comcloud.google.com/­datastorehughestech.com.au/­products/­msqlwww.oracle.com/­database/­technologies/­related/­berkeleydb.html
Technical documentationdocs.databricks.comcloud.google.com/­datastore/­docsdocs.oracle.com/­cd/­E17076_05/­html/­index.html
DeveloperDatabricksGoogleHughes TechnologiesOracle infooriginally developed by Sleepycat, which was acquired by Oracle
Initial release2013200819941994
Current release4.4, October 202118.1.40, May 2020
License infoCommercial or Open Sourcecommercialcommercialcommercial infofree licenses can be providedOpen Source infocommercial license available
Cloud-based only infoOnly available as a cloud serviceyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCC, Java, C++ (depending on the Berkeley DB edition)
Server operating systemshostedhostedAIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows
AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeyesschema-free
Typing infopredefined data types such as float or dateyes, details hereyesno
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.yesnonoyes infoonly with the Berkeley DB XML edition
Secondary indexesyesyesyesyes
SQL infoSupport of SQLwith Databricks SQLSQL-like query language (GQL)A subset of ANSI SQL is implemented infono subqueries, aggregate functions, views, foreign keys, triggersyes infoSQL interfaced based on SQLite is available
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
Supported programming languagesPython
R
Scala
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C++
Delphi
Java
Perl
PHP
Tcl
.Net infoFigaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others infoThird-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js) info3rd party binding
Perl
Python
Tcl
Server-side scripts infoStored proceduresuser defined functions and aggregatesusing Google App Enginenono
TriggersCallbacks using the Google Apps Enginenoyes infoonly for the SQL API
Partitioning methods infoMethods for storing different data on different nodesShardingnonenone
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication using PaxosnoneSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflownono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate 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.none
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesno
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.nononoyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)nono
More information provided by the system vendor
DatabricksGoogle Cloud DatastoremSQL infoMini SQLOracle Berkeley DB
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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DatabricksGoogle Cloud DatastoremSQL infoMini SQLOracle Berkeley DB
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