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DBMS > Apache Impala vs. Databricks vs. Faircom DB vs. Stardog

System Properties Comparison Apache Impala vs. Databricks vs. Faircom DB vs. Stardog

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonDatabricks  Xexclude from comparisonFaircom DB infoformerly c-treeACE  Xexclude from comparisonStardog  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.Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization
Primary database modelRelational DBMSDocument store
Relational DBMS
Key-value store
Relational DBMS
Graph DBMS
RDF store
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
Score0.20
Rank#318  Overall
#48  Key-value stores
#141  Relational DBMS
Score2.02
Rank#123  Overall
#11  Graph DBMS
#6  RDF stores
Websiteimpala.apache.orgwww.databricks.comwww.faircom.com/­products/­faircom-dbwww.stardog.com
Technical documentationimpala.apache.org/­impala-docs.htmldocs.databricks.comdocs.faircom.com/­docs/­en/­UUID-7446ae34-a1a7-c843-c894-d5322e395184.htmldocs.stardog.com
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaDatabricksFairCom CorporationStardog-Union
Initial release2013201319792010
Current release4.1.0, June 2022V12, November 20207.3.0, May 2020
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercial infoRestricted, free version availablecommercial info60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++ANSI C, C++Java
Server operating systemsLinuxhostedAIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows infoeasily portable to other OSs
Linux
macOS
Windows
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)schema free, schema optional, schema required, partial schema,schema-free and OWL/RDFS-schema support
Typing infopredefined data types such as float or dateyesyes, ANSI SQL Types, JSON, typed binary structuresyes
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.noyesnono infoImport/export of XML data possible
Secondary indexesyesyesyesyes infosupports real-time indexing in full-text and geospatial
SQL infoSupport of SQLSQL-like DML and DDL statementswith Databricks SQLyes, ANSI SQL with proprietary extensionsYes, compatible with all major SQL variants through dedicated BI/SQL Server
APIs and other access methodsJDBC
ODBC
JDBC
ODBC
RESTful HTTP API
ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC
GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3
Supported programming languagesAll languages supporting JDBC/ODBCPython
R
Scala
.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic
.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functions and aggregatesyes info.Net, JavaScript, C/C++user defined functions and aggregates, HTTP Server extensions in Java
Triggersnoyesyes infovia event handlers
Partitioning methods infoMethods for storing different data on different nodesShardingFile partitioning, horizontal partitioning, sharding infoCustomizable business rules for table partitioningnone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryesyes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).Multi-source replication in HA-Cluster
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction
Immediate Consistency in HA-Cluster
Foreign keys infoReferential integritynoyesyes inforelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDtunable from ACID to Eventually ConsistentACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesYes, tunable from durable to delayed durability to in-memoryyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosFine grained access rights according to SQL-standard with additional protections for filesAccess rights for users and roles
More information provided by the system vendor
Apache ImpalaDatabricksFaircom DB infoformerly c-treeACEStardog
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
Apache ImpalaDatabricksFaircom DB infoformerly c-treeACEStardog
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