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DBMS > Databricks vs. Hazelcast vs. SwayDB vs. Tkrzw

System Properties Comparison Databricks vs. Hazelcast vs. SwayDB vs. Tkrzw

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonHazelcast  Xexclude from comparisonSwayDB  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  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.A widely adopted in-memory data gridAn embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storageA concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet
Primary database modelDocument store
Relational DBMS
Key-value storeKey-value storeKey-value store
Secondary database modelsDocument store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score5.46
Rank#61  Overall
#7  Key-value stores
Score0.04
Rank#387  Overall
#61  Key-value stores
Score0.07
Rank#372  Overall
#57  Key-value stores
Websitewww.databricks.comhazelcast.comswaydb.simer.audbmx.net/­tkrzw
Technical documentationdocs.databricks.comhazelcast.org/­imdg/­docs
DeveloperDatabricksHazelcastSimer PlahaMikio Hirabayashi
Initial release2013200820182020
Current release5.3.6, November 20230.9.3, August 2020
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2; commercial licenses availableOpen Source infoGNU Affero GPL V3.0Open Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaScalaC++
Server operating systemshostedAll OS with a Java VMLinux
macOS
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeschema-freeschema-free
Typing infopredefined data types such as float or dateyesnono
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.yesyes infothe object must implement a serialization strategynono
Secondary indexesyesyesno
SQL infoSupport of SQLwith Databricks SQLSQL-like query languagenono
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JCache
JPA
Memcached protocol
RESTful HTTP API
Supported programming languagesPython
R
Scala
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
Java
Kotlin
Scala
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresuser defined functions and aggregatesyes infoEvent Listeners, Executor Servicesnono
Triggersyes infoEventsnono
Partitioning methods infoMethods for storing different data on different nodesShardingnonenone
Replication methods infoMethods for redundantly storing data on multiple nodesyesyes infoReplicated Mapnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDone or two-phase-commit; repeatable reads; read commitedAtomic execution of operations
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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.noyesyesyes infousing specific database classes
User concepts infoAccess controlRole-based access controlnono
More information provided by the system vendor
DatabricksHazelcastSwayDBTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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DatabricksHazelcastSwayDBTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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