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DBMS > Amazon DocumentDB vs. Apache IoTDB vs. Databricks vs. Faircom DB

System Properties Comparison Amazon DocumentDB vs. Apache IoTDB vs. Databricks vs. Faircom DB

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Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonApache IoTDB  Xexclude from comparisonDatabricks  Xexclude from comparisonFaircom DB infoformerly c-treeACE  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkThe 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.
Primary database modelDocument storeTime Series DBMSDocument store
Relational DBMS
Key-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.91
Rank#124  Overall
#22  Document stores
Score1.31
Rank#159  Overall
#14  Time Series DBMS
Score84.24
Rank#14  Overall
#2  Document stores
#9  Relational DBMS
Score0.18
Rank#314  Overall
#45  Key-value stores
#141  Relational DBMS
Websiteaws.amazon.com/­documentdbiotdb.apache.orgwww.databricks.comwww.faircom.com/­products/­faircom-db
Technical documentationaws.amazon.com/­documentdb/­resourcesiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.databricks.comdocs.faircom.com/­docs/­en/­UUID-7446ae34-a1a7-c843-c894-d5322e395184.html
DeveloperApache Software FoundationDatabricksFairCom Corporation
Initial release2019201820131979
Current release1.1.0, April 2023V13, July 2024
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0commercialcommercial infoRestricted, free version available
Cloud-based only infoOnly available as a cloud serviceyesnoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaANSI C, C++
Server operating systemshostedAll OS with a Java VM (>= 1.8)hostedAIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows infoeasily portable to other OSs
Data schemeschema-freeyesFlexible Schema (defined schema, partial schema, schema free)schema free, schema optional, schema required, partial schema,
Typing infopredefined data types such as float or dateyesyesyes, ANSI SQL Types, JSON, typed binary structures
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.nonoyesno
Secondary indexesyesyesyesyes
SQL infoSupport of SQLnoSQL-like query languagewith Databricks SQLyes, ANSI SQL with proprietary extensions
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)JDBC
Native API
JDBC
ODBC
RESTful HTTP API
ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
C
C#
C++
Go
Java
Python
Scala
Python
R
Scala
.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic
Server-side scripts infoStored proceduresnoyesuser defined functions and aggregatesyes info.Net, JavaScript, C/C++
Triggersnoyesyes
Partitioning methods infoMethods for storing different data on different nodesnonehorizontal partitioning (by time range) + vertical partitioning (by deviceId)File partitioning, horizontal partitioning, sharding infoCustomizable business rules for table partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones for high availability, asynchronous replication for up to 15 read replicasselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasyesyes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)Integration with Hadoop and Sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyEventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possiblenoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsnoACIDtunable from ACID to Eventually Consistent
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesYes, tunable from durable to delayed durability to in-memory
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes
User concepts infoAccess controlAccess rights for users and rolesyesFine grained access rights according to SQL-standard with additional protections for files
More information provided by the system vendor
Amazon DocumentDBApache IoTDBDatabricksFaircom DB infoformerly c-treeACE
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
Amazon DocumentDBApache IoTDBDatabricksFaircom DB infoformerly c-treeACE
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