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DBMS > Databricks vs. PouchDB vs. Splice Machine

System Properties Comparison Databricks vs. PouchDB vs. Splice Machine

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonPouchDB  Xexclude from comparisonSplice Machine  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.JavaScript DBMS with an API inspired by CouchDBOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelDocument store
Relational DBMS
Document storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score2.28
Rank#115  Overall
#21  Document stores
Score0.54
Rank#250  Overall
#114  Relational DBMS
Websitewww.databricks.compouchdb.comsplicemachine.com
Technical documentationdocs.databricks.compouchdb.com/­guidessplicemachine.com/­how-it-works
DeveloperDatabricksApache Software FoundationSplice Machine
Initial release201320122014
Current release7.1.1, June 20193.1, March 2021
License infoCommercial or Open SourcecommercialOpen SourceOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaScriptJava
Server operating systemshostedserver-less, requires a JavaScript environment (browser, Node.js)Linux
OS X
Solaris
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeyes
Typing infopredefined data types such as float or datenoyes
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.yesno
Secondary indexesyesyes infovia viewsyes
SQL infoSupport of SQLwith Databricks SQLnoyes
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
HTTP REST infoonly for PouchDB Server
JavaScript API
JDBC
Native Spark Datasource
ODBC
Supported programming languagesPython
R
Scala
JavaScriptC#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functions and aggregatesView functions in JavaScriptyes infoJava
Triggersyesyes
Partitioning methods infoMethods for storing different data on different nodesSharding infowith a proxy-based framework, named couchdb-loungeShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication infoalso with CouchDB databases
Source-replica replication infoalso with CouchDB databases
Multi-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyes infoby using IndexedDB, WebSQL or LevelDB as backendyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes
User concepts infoAccess controlnoAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
DatabricksPouchDBSplice Machine
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksPouchDBSplice Machine
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