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DBMS > Apache Impala vs. Databricks vs. Tigris

System Properties Comparison Apache Impala vs. Databricks vs. Tigris

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonDatabricks  Xexclude from comparisonTigris  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 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
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score11.68
Rank#41  Overall
#25  Relational DBMS
Score102.66
Rank#12  Overall
#2  Document stores
#8  Relational DBMS
Score0.06
Rank#352  Overall
#50  Document stores
#53  Key-value stores
#23  Search engines
#35  Time Series DBMS
Websiteimpala.apache.orgwww.databricks.comwww.tigrisdata.com
Technical documentationimpala.apache.org/­impala-docs.htmldocs.databricks.comwww.tigrisdata.com/­docs
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaDatabricksTigris Data, Inc.
Initial release201320132022
Current release4.1.0, June 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialOpen 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 languageC++
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 SQLSQL-like DML and DDL statementswith Databricks SQLno
APIs and other access methodsJDBC
ODBC
JDBC
ODBC
RESTful HTTP API
CLI Client
gRPC
RESTful HTTP API
Supported programming languagesAll languages supporting JDBC/ODBCPython
R
Scala
Go
Java
JavaScript (Node.js)
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functions and aggregatesno
Triggersnono
Partitioning methods infoMethods for storing different data on different nodesShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryesyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduce
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 datanoACIDACID
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, groups and roles infobased on Apache Sentry and KerberosAccess rights for users and roles

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Apache ImpalaDatabricksTigris
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