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DBMS > Apache Impala vs. Databricks vs. EJDB vs. eXtremeDB vs. Sequoiadb

System Properties Comparison Apache Impala vs. Databricks vs. EJDB vs. eXtremeDB vs. Sequoiadb

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonDatabricks  Xexclude from comparisonEJDB  Xexclude from comparisoneXtremeDB  Xexclude from comparisonSequoiadb  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.Embeddable document-store database library with JSON representation of queries (in MongoDB style)Natively in-memory DBMS with options for persistency, high-availability and clusteringNewSQL database with distributed OLTP and SQL
Primary database modelRelational DBMSDocument store
Relational DBMS
Document storeRelational DBMS
Time Series DBMS
Document store
Relational DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score12.45
Rank#40  Overall
#24  Relational DBMS
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score0.31
Rank#296  Overall
#44  Document stores
Score0.80
Rank#214  Overall
#99  Relational DBMS
#18  Time Series DBMS
Score0.50
Rank#258  Overall
#41  Document stores
#120  Relational DBMS
Websiteimpala.apache.orgwww.databricks.comgithub.com/­Softmotions/­ejdbwww.mcobject.comwww.sequoiadb.com
Technical documentationimpala.apache.org/­impala-docs.htmldocs.databricks.comgithub.com/­Softmotions/­ejdb/­blob/­master/­README.mdwww.mcobject.com/­docs/­extremedb.htmwww.sequoiadb.com/­en/­index.php?m=Files&a=index
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaDatabricksSoftmotionsMcObjectSequoiadb Ltd.
Initial release20132013201220012013
Current release4.1.0, June 20228.2, 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialOpen Source infoGPLv2commercialOpen Source infoServer: AGPL; Client: Apache V2
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++CC and C++C++
Server operating systemsLinuxhostedserver-lessAIX
HP-UX
Linux
macOS
Solaris
Windows
Linux
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)schema-freeyesschema-free
Typing infopredefined data types such as float or dateyesyes infostring, integer, double, bool, date, object_idyesyes infooid, date, timestamp, binary, regex
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 infosupport of XML interfaces availableno
Secondary indexesyesyesnoyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementswith Databricks SQLnoyes infowith the option: eXtremeSQLSQL-like query language
APIs and other access methodsJDBC
ODBC
JDBC
ODBC
RESTful HTTP API
in-process shared library.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API
proprietary protocol using JSON
Supported programming languagesAll languages supporting JDBC/ODBCPython
R
Scala
Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby
.Net
C
C#
C++
Java
Lua
Python
Scala
.Net
C++
Java
PHP
Python
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functions and aggregatesnoyesJavaScript
Triggersnonoyes infoby defining eventsno
Partitioning methods infoMethods for storing different data on different nodesShardingnonehorizontal partitioning / shardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryesnoneActive Replication Fabric™ for IoT
Multi-source replication infoby means of eXtremeDB Cluster option
Source-replica replication infoby means of eXtremeDB High Availability option
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynono infotypically not needed, however similar functionality with collection joins possibleyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoACIDDocument is locked during a transaction
Concurrency infoSupport for concurrent manipulation of datayesyesyes infoRead/Write Lockingyes infoOptimistic (MVCC) and pessimistic (locking) strategies availableyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesno
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosnosimple password-based access control
More information provided by the system vendor
Apache ImpalaDatabricksEJDBeXtremeDBSequoiadb
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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eXtremeDB is an in-memory and/or persistent database system that offers an ultra-small...
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Competitive advantageseXtremeDB databases can be modeled relationally or as objects and can utilize SQL...
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Typical application scenariosIoT application across all markets: Industrial Control, Netcom, Telecom, Defense,...
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Key customersSchneider Electronics, F5 Networks, TNS, Boeing, Northrop Grumman, GoPro, ViaSat,...
» more
Market metricsWith hundreds of customers and over 30 million devices/applications using the product...
» more
Licensing and pricing modelsFor server use cases, there is a simple per-server license irrespective of the number...
» more

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Apache ImpalaDatabricksEJDBeXtremeDBSequoiadb
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