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

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

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonDatabricks  Xexclude from comparisonFaircom DB infoformerly c-treeACE  Xexclude from comparisonTerarkDB  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.A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB
Primary database modelRelational DBMSDocument store
Relational DBMS
Key-value store
Relational DBMS
Key-value store
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
Score0.29
Rank#304  Overall
#43  Key-value stores
#136  Relational DBMS
Score0.08
Rank#367  Overall
#56  Key-value stores
Websiteimpala.apache.orgwww.databricks.comwww.faircom.com/­products/­faircom-dbgithub.com/­bytedance/­terarkdb
Technical documentationimpala.apache.org/­impala-docs.htmldocs.databricks.comdocs.faircom.com/­docs/­en/­UUID-7446ae34-a1a7-c843-c894-d5322e395184.htmlbytedance.larkoffice.com/­docs/­doccnZmYFqHBm06BbvYgjsHHcKc
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaDatabricksFairCom CorporationByteDance, originally Terark
Initial release2013201319792016
Current release4.1.0, June 2022V12, November 2020
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercial infoRestricted, free version availablecommercial inforestricted open source version available
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++C++
Server operating systemsLinuxhostedAIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows infoeasily portable to other OSs
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)schema free, schema optional, schema required, partial schema,schema-free
Typing infopredefined data types such as float or dateyesyes, ANSI SQL Types, JSON, typed binary structuresno
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
Secondary indexesyesyesyesno
SQL infoSupport of SQLSQL-like DML and DDL statementswith Databricks SQLyes, ANSI SQL with proprietary extensionsno
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
C++ API
Java API
Supported programming languagesAll languages supporting JDBC/ODBCPython
R
Scala
.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic
C++
Java
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functions and aggregatesyes info.Net, JavaScript, C/C++no
Triggersnoyesno
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).none
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
Foreign keys infoReferential integritynoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDtunable from ACID to Eventually Consistentno
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 filesno
More information provided by the system vendor
Apache ImpalaDatabricksFaircom DB infoformerly c-treeACETerarkDB
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
Apache ImpalaDatabricksFaircom DB infoformerly c-treeACETerarkDB
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