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DBMS > Blueflood vs. Microsoft Azure Cosmos DB vs. Splice Machine

System Properties Comparison Blueflood vs. Microsoft Azure Cosmos DB vs. Splice Machine

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Editorial information provided by DB-Engines
NameBlueflood  Xexclude from comparisonMicrosoft Azure Cosmos DB infoformer name was Azure DocumentDB  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionScalable TimeSeries DBMS based on CassandraGlobally distributed, horizontally scalable, multi-model database serviceOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelTime Series DBMSDocument store
Graph DBMS
Key-value store
Wide column store
Relational DBMS
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.08
Rank#308  Overall
#28  Time Series DBMS
Score39.71
Rank#26  Overall
#3  Document stores
#2  Graph DBMS
#3  Key-value stores
#3  Wide column stores
Score0.87
Rank#185  Overall
#89  Relational DBMS
Websiteblueflood.ioazure.microsoft.com/­services/­cosmos-dbsplicemachine.com
Technical documentationgithub.com/­rackerlabs/­blueflood/­wikidocs.microsoft.com/­azure/­cosmos-dbdoc.splicemachine.com
DeveloperRackspaceMicrosoftSplice Machine
Initial release201320142014
Current release3.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJava
Server operating systemsLinux
OS X
hostedLinux
OS X
Solaris
Windows
Data schemepredefined schemeschema-freeyes
Typing infopredefined data types such as float or dateyesyes infoJSON typesyes
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.no
Secondary indexesnoyes infoAll properties auto-indexed by defaultyes
SQL infoSupport of SQLnoSQL-like query languageyes
APIs and other access methodsHTTP RESTDocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API
JDBC
Native Spark Datasource
ODBC
Supported programming languages.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresnoJavaScriptyes infoJava
TriggersnoJavaScriptyes
Partitioning methods infoMethods for storing different data on different nodesSharding infobased on CassandraSharding infoImplicit feature of the cloud serviceShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor infobased on Cassandrayes infoImplicit feature of the cloud serviceMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnowith Hadoop integration infoIntegration with Hadoop/HDInsight on Azure*Yes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency infoConsistency level configurable on request level
Session Consistency
Immediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoMulti-item ACID transactions with snapshot isolation within a partitionACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controlnoAccess rights can be defined down to the item levelAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
BluefloodMicrosoft Azure Cosmos DB infoformer name was Azure DocumentDBSplice Machine
News

How to Break Data Silos to Drive Enterprise-Wide AI
11 June 2021

Point-in-Time Correctness in Machine Learning
1 June 2021

The Compliance Nightmare Lurking in Your Data Science Team
6 May 2021

Whose Job Is It Anyway?
23 April 2021

How Optimizing MLOps Can Revolutionize Enterprise AI
15 April 2021

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More resources
BluefloodMicrosoft Azure Cosmos DB infoformer name was Azure DocumentDBSplice Machine
Recent citations in the news

ChaosDB: Infosec bods could pull anyone's plaintext Azure Cosmos DB keys at will from Microsoft admin tools
12 November 2021, The Register

Researchers discover critical flaw in Azure Cosmos DB
27 August 2021, TechTarget

Microsoft Azure Cosmos DB Incident Underscores the Need to Closely Watch Cloud Data
30 August 2021, Dark Reading

Microsoft announces several new updates for Azure Cosmos DB - MSPoweruser
25 May 2021, MSPoweruser

Azure Cosmos DB Conf - Microsoft
21 April 2021, Channel 9

provided by Google News

Splice Machine Announces New Capabilities to Support Real-Time AI with Version 3.1 of its Scale-Out SQL Database
9 March 2021, PRNewswire

Splice Machine Launches the Splice Machine Feature Store to Simplify Feature Engineering and Democratize Machine Learning
19 January 2021, PRNewswire

Machine learning data pipeline outfit Splice Machine files for insolvency
26 August 2021, The Register

Splice Machine database travels full circle to predictive application platform
25 September 2017, ZDNet

Splice Machine Introduces New Edition of Livewire Operational AI Platform for Industrial Users
25 May 2021, PRNewswire

provided by Google News

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