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DBMS > Apache Impala vs. Databricks vs. Kyligence Enterprise

System Properties Comparison Apache Impala vs. Databricks vs. Kyligence Enterprise

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonDatabricks  Xexclude from comparisonKyligence Enterprise  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopThe 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 distributed analytics engine for big data, built on top of Apache Kylin
Primary database modelRelational DBMSDocument store
Relational DBMS
Relational DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score0.40
Rank#269  Overall
#124  Relational DBMS
Websiteimpala.apache.orgwww.databricks.comkyligence.io/­kyligence-enterprise
Technical documentationimpala.apache.org/­impala-docs.htmldocs.databricks.com
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaDatabricksKyligence, Inc.
Initial release201320132016
Current release4.1.0, June 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercial
Cloud-based only infoOnly available as a cloud servicenoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++Java
Server operating systemsLinuxhostedLinux
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 SQLSQL-like DML and DDL statementswith Databricks SQLANSI SQL for queries (using Apache Calcite)
APIs and other access methodsJDBC
ODBC
JDBC
ODBC
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
Supported programming languagesAll languages supporting JDBC/ODBCPython
R
Scala
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functions and aggregates
Triggersno
Partitioning methods infoMethods for storing different data on different nodesSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceyes
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberos
More information provided by the system vendor
Apache ImpalaDatabricksKyligence Enterprise
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
Apache ImpalaDatabricksKyligence Enterprise
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