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DBMS > Amazon DynamoDB vs. Apache Phoenix vs. Faircom DB vs. IBM Db2 Event Store vs. Splice Machine

System Properties Comparison Amazon DynamoDB vs. Apache Phoenix vs. Faircom DB vs. IBM Db2 Event Store vs. Splice Machine

Editorial information provided by DB-Engines
NameAmazon DynamoDB  Xexclude from comparisonApache Phoenix  Xexclude from comparisonFaircom DB infoformerly c-treeACE  Xexclude from comparisonIBM Db2 Event Store  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionHosted, scalable database service by Amazon with the data stored in Amazons cloudA scale-out RDBMS with evolutionary schema built on Apache HBaseNative high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.Distributed Event Store optimized for Internet of Things use casesOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelDocument store
Key-value store
Relational DBMSKey-value store
Relational DBMS
Event Store
Time Series DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score77.72
Rank#16  Overall
#2  Document stores
#2  Key-value stores
Score2.10
Rank#131  Overall
#63  Relational DBMS
Score0.31
Rank#298  Overall
#42  Key-value stores
#137  Relational DBMS
Score0.25
Rank#320  Overall
#2  Event Stores
#28  Time Series DBMS
Score0.54
Rank#257  Overall
#118  Relational DBMS
Websiteaws.amazon.com/­dynamodbphoenix.apache.orgwww.faircom.com/­products/­faircom-dbwww.ibm.com/­products/­db2-event-storesplicemachine.com
Technical documentationdocs.aws.amazon.com/­dynamodbphoenix.apache.orgdocs.faircom.com/­docs/­en/­UUID-7446ae34-a1a7-c843-c894-d5322e395184.htmlwww.ibm.com/­docs/­en/­db2-event-storesplicemachine.com/­how-it-works
DeveloperAmazonApache Software FoundationFairCom CorporationIBMSplice Machine
Initial release20122014197920172014
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 2019V12, November 20202.03.1, March 2021
License infoCommercial or Open Sourcecommercial infofree tier for a limited amount of database operationsOpen Source infoApache Version 2.0commercial infoRestricted, free version availablecommercial infofree developer edition availableOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesnononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaANSI C, C++C and C++Java
Server operating systemshostedLinux
Unix
Windows
AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows infoeasily portable to other OSs
Linux infoLinux, macOS, Windows for the developer additionLinux
OS X
Solaris
Windows
Data schemeschema-freeyes infolate-bound, schema-on-read capabilitiesschema free, schema optional, schema required, partial schema,yesyes
Typing infopredefined data types such as float or dateyesyesyes, ANSI SQL Types, JSON, typed binary structuresyesyes
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.nonono
Secondary indexesyesyesyesnoyes
SQL infoSupport of SQLnoyesyes, ANSI SQL with proprietary extensionsyes infothrough the embedded Spark runtimeyes
APIs and other access methodsRESTful HTTP APIJDBCADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC
ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API
JDBC
Native Spark Datasource
ODBC
Supported programming languages.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby
C
C#
C++
Go
Groovy
Java
PHP
Python
Scala
.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic
C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresnouser defined functionsyes info.Net, JavaScript, C/C++yesyes infoJava
Triggersyes infoby integration with AWS Lambdanoyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingFile partitioning, horizontal partitioning, sharding infoCustomizable business rules for table partitioningShardingShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication
Source-replica replication
yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).Active-active shard replicationMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)Hadoop integrationnonoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency infocan be specified for read operations
Immediate Consistency or Eventual ConsistencyEventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction
Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoACID across one or more tables within a single AWS account and regionACIDtunable from ACID to Eventually ConsistentnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesNo - written data is immutableyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesYes, tunable from durable to delayed durability to in-memoryYes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storageyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesyesyes
User concepts infoAccess controlAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyFine grained access rights according to SQL-standard with additional protections for filesfine grained access rights according to SQL-standardAccess rights for users, groups and roles according to SQL-standard

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More resources
Amazon DynamoDBApache PhoenixFaircom DB infoformerly c-treeACEIBM Db2 Event StoreSplice Machine
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