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DBMS > Apache Kylin vs. Databricks vs. Tigris

System Properties Comparison Apache Kylin vs. Databricks vs. Tigris

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Editorial information provided by DB-Engines
NameApache Kylin  Xexclude from comparisonDatabricks  Xexclude from comparisonTigris  Xexclude from comparison
DescriptionA distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stackThe 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 horizontally scalable, ACID transactional, document database available both as a fully managed cloud service and for deployment on self-managed infrastructure
Primary database modelRelational DBMSDocument store
Relational DBMS
Document store
Key-value store
Search engine
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#172  Overall
#79  Relational DBMS
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score0.02
Rank#369  Overall
#51  Document stores
#55  Key-value stores
#24  Search engines
#38  Time Series DBMS
Websitekylin.apache.orgwww.databricks.comwww.tigrisdata.com
Technical documentationkylin.apache.org/­docsdocs.databricks.comwww.tigrisdata.com/­docs
DeveloperApache Software Foundation, originally contributed from eBay IncDatabricksTigris Data, Inc.
Initial release201520132022
Current release3.1.0, July 2020
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava
Server operating systemsLinuxhostedLinux
macOS
Windows
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)yes
Typing infopredefined data types such as float or dateyesyes
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.noyesno
Secondary indexesyesyesyes
SQL infoSupport of SQLANSI SQL for queries (using Apache Calcite)with Databricks SQLno
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
CLI Client
gRPC
RESTful HTTP API
Supported programming languagesPython
R
Scala
Go
Java
JavaScript (Node.js)
Server-side scripts infoStored proceduresuser defined functions and aggregatesno
Triggersno
Partitioning methods infoMethods for storing different data on different nodesSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes, using FoundationDB
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlAccess rights for users and roles
More information provided by the system vendor
Apache KylinDatabricksTigris
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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Apache KylinDatabricksTigris
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