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DBMS > BigObject vs. ScyllaDB vs. Spark SQL vs. Trafodion

System Properties Comparison BigObject vs. ScyllaDB vs. Spark SQL vs. Trafodion

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Editorial information provided by DB-Engines
NameBigObject  Xexclude from comparisonScyllaDB  Xexclude from comparisonSpark SQL  Xexclude from comparisonTrafodion  Xexclude from comparison
Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.
DescriptionAnalytic DBMS for real-time computations and queriesCassandra and DynamoDB compatible wide column storeSpark SQL is a component on top of 'Spark Core' for structured data processingTransactional SQL-on-Hadoop DBMS
Primary database modelRelational DBMS infoa hierachical model (tree) can be imposedWide column storeRelational DBMSRelational DBMS
Secondary database modelsKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.19
Rank#329  Overall
#146  Relational DBMS
Score4.08
Rank#76  Overall
#5  Wide column stores
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitebigobject.iowww.scylladb.comspark.apache.org/­sqltrafodion.apache.org
Technical documentationdocs.bigobject.iodocs.scylladb.comspark.apache.org/­docs/­latest/­sql-programming-guide.htmltrafodion.apache.org/­documentation.html
DeveloperBigObject, Inc.ScyllaDBApache Software FoundationApache Software Foundation, originally developed by HP
Initial release2015201520142014
Current releaseScyllaDB Open Source 5.4.1, January 20243.5.0 ( 2.13), September 20232.3.0, February 2019
License infoCommercial or Open Sourcecommercial infofree community edition availableOpen Source infoOpen Source (AGPL), commercial license availableOpen Source infoApache 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Scylla Cloud: Create real-time applications that run at global scale with Scylla Cloud, the industry’s most powerful NoSQL DBaaS
Implementation languageC++ScalaC++, Java
Server operating systemsLinux infodistributed as a docker-image
OS X infodistributed as a docker-image (boot2docker)
Windows infodistributed as a docker-image (boot2docker)
LinuxLinux
OS X
Windows
Linux
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesyesyes
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.nononono
Secondary indexesyesyes infocluster global secondary indicesnoyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like DML and DDL statements (CQL)SQL-like DML and DDL statementsyes
APIs and other access methodsfluentd
ODBC
RESTful HTTP API
Proprietary protocol (CQL) infocompatible with CQL (Cassandra Query Language, an SQL-like language)
RESTful HTTP API (DynamoDB compatible)
Thrift
JDBC
ODBC
ADO.NET
JDBC
ODBC
Supported programming languagesFor CQL interface: C#, C++, Clojure, Erlang, Go, Haskell, Java, JavaScript, Node.js, Perl, PHP, Python, Ruby, Rust, Scala
For DynamoDB interface: .Net, ColdFusion, Erlang, Groovy, Java, JavaScript, Perl, PHP, Python, Ruby
Java
Python
R
Scala
All languages supporting JDBC/ODBC/ADO.Net
Server-side scripts infoStored proceduresLuayes, LuanoJava Stored Procedures
Triggersnononono
Partitioning methods infoMethods for storing different data on different nodesnoneShardingyes, utilizing Spark CoreSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnoneselectable replication factor infoRepresentation of geographical distribution of servers is possiblenoneyes, via HBase
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes infovia user defined functions and HBase
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneEventual Consistency
Tunable Consistency infocan be individually decided for each write operation
Immediate Consistency
Foreign keys infoReferential integrityyes infoautomatically between fact table and dimension tablesnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono infoAtomicity and isolation are supported for single operationsnoACID
Concurrency infoSupport for concurrent manipulation of datayes infoRead/write lock on objects (tables, trees)yesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes infoin-memory tablesnono
User concepts infoAccess controlnoAccess rights for users can be defined per objectnofine grained access rights according to SQL-standard
More information provided by the system vendor
BigObjectScyllaDBSpark SQLTrafodion
Specific characteristicsScyllaDB is engineered to deliver predictable performance at scale. It’s adopted...
» more
Competitive advantagesHighly-performant (efficiently utilizes full resources of a node and network; millions...
» more
Typical application scenariosScyllaDB is ideal for applications that require high throughput and low latency at...
» more
Key customersDiscord, Epic Games, Expedia, Zillow, Comcast, Disney+ Hotstar, Samsung, ShareChat,...
» more
Market metricsScyllaDB typically offers ~75% total cost of ownership savings, with ~5X higher throughput...
» more
Licensing and pricing modelsScyllaDB Open Source - free open source software (AGPL) ScyllaDB Enterprise - subscription-based...
» more

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BigObjectScyllaDBSpark SQLTrafodion
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