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DBMS > Databricks vs. Google Cloud Bigtable vs. Lovefield vs. Riak TS vs. Splice Machine

System Properties Comparison Databricks vs. Google Cloud Bigtable vs. Lovefield vs. Riak TS vs. Splice Machine

Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonLovefield  Xexclude from comparisonRiak TS  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.Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Embeddable relational database for web apps written in pure JavaScriptRiak TS is a distributed NoSQL database optimized for time series data and based on Riak KVOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelDocument store
Relational DBMS
Key-value store
Wide column store
Relational DBMSTime Series DBMSRelational 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
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score0.33
Rank#286  Overall
#131  Relational DBMS
Score0.28
Rank#307  Overall
#27  Time Series DBMS
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websitewww.databricks.comcloud.google.com/­bigtablegoogle.github.io/­lovefieldsplicemachine.com
Technical documentationdocs.databricks.comcloud.google.com/­bigtable/­docsgithub.com/­google/­lovefield/­blob/­master/­docs/­spec_index.mdwww.tiot.jp/­riak-docs/­riak/­ts/­latestsplicemachine.com/­how-it-works
DeveloperDatabricksGoogleGoogleOpen Source, formerly Basho TechnologiesSplice Machine
Initial release20132015201420152014
Current release2.1.12, February 20173.0.0, September 20223.1, March 2021
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache 2.0Open SourceOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaScriptErlangJava
Server operating systemshostedhostedserver-less, requires a JavaScript environment (browser, Node.js) infotested with Chrome, Firefox, IE, SafariLinux
OS X
Linux
OS X
Solaris
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeyesschema-freeyes
Typing infopredefined data types such as float or datenoyesnoyes
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 indexesyesnoyesrestrictedyes
SQL infoSupport of SQLwith Databricks SQLnoSQL-like query language infovia JavaScript builder patternyes, limitedyes
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
HTTP API
Native Erlang Interface
JDBC
Native Spark Datasource
ODBC
Supported programming languagesPython
R
Scala
C#
C++
Go
Java
JavaScript (Node.js)
Python
JavaScriptC infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functions and aggregatesnonoErlangyes infoJava
TriggersnoUsing read-only observersyes infopre-commit hooks and post-commit hooksyes
Partitioning methods infoMethods for storing different data on different nodesShardingnoneShardingShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyesInternal replication in Colossus, and regional replication between two clusters in different zonesnoneselectable replication factorMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnoyesYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesno infolinks between datasets can be storedyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic single-row operationsACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyes, by using IndexedDB or the cloud service Firebase Realtime Databaseyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes infousing MemoryDByes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)nonoAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
DatabricksGoogle Cloud BigtableLovefieldRiak TSSplice Machine
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksGoogle Cloud BigtableLovefieldRiak TSSplice Machine
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