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DBMS > FatDB vs. Spark SQL vs. Teradata vs. YugabyteDB

System Properties Comparison FatDB vs. Spark SQL vs. Teradata vs. YugabyteDB

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Editorial information provided by DB-Engines
NameFatDB  Xexclude from comparisonSpark SQL  Xexclude from comparisonTeradata  Xexclude from comparisonYugabyteDB  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
DescriptionA .NET NoSQL DBMS that can integrate with and extend SQL Server.Spark SQL is a component on top of 'Spark Core' for structured data processingA hybrid cloud data analytics software platform (Teradata Vantage)High-performance distributed SQL database for global, internet-scale applications. Wire and feature compatible with PostgreSQL.
Primary database modelDocument store
Key-value store
Relational DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument store
Graph DBMS
Spatial DBMS
Time Series DBMS
Document store
Wide column store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score22.86
Rank#35  Overall
#21  Relational DBMS
Score68.39
Rank#18  Overall
#12  Relational DBMS
Score2.68
Rank#112  Overall
#57  Relational DBMS
Websitespark.apache.org/­sqlwww.teradata.comwww.yugabyte.com
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.teradata.comdocs.yugabyte.com
github.com/­yugabyte/­yugabyte-db
DeveloperFatCloudApache Software FoundationTeradataYugabyte Inc.
Initial release2012201419842017
Current release3.2.0, October 2021Teradata Vantage 1.0 MU2, January 20192.6, August 2021
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercialOpen 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.
Yugabyte Cloud is the fully managed database-as-a-service offering of YugabyteDB. Get started quickly, and effortlessly ensure continuous availability and limitless scale of your cloud native applications.
Implementation languageC#ScalaC and C++
Server operating systemsWindowsLinux
OS X
Windows
hosted
Linux
Linux
OS X
Data schemeschema-freeyesyesdepending on used data model
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.noyesno
Secondary indexesyesnoyes infoJoin-index to prejoin tables, aggregate index, sparse index, hash indexyes
SQL infoSupport of SQLno infoVia inetgration in SQL ServerSQL-like DML and DDL statementsyes infoSQL 2016 + extensionsyes, PostgreSQL compatible
APIs and other access methods.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
JDBC
ODBC
.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB
JDBC
YCQL, an SQL-based flexible-schema API with its roots in Cassandra Query Language
YSQL - a fully relational SQL API that is wire compatible with the SQL language in PostgreSQL
Supported programming languagesC#Java
Python
R
Scala
C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby
C
C#
C++
Go
Java
JavaScript (Node.js)
Python
Ruby
Server-side scripts infoStored proceduresyes infovia applicationsnoyes infoUDFs, stored procedures, table functions in parallelyes infosql, plpgsql, C
Triggersyes infovia applicationsnoyesyes
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark CoreSharding infoHashingHash and Range Sharding, row-level geo-partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factornoneMulti-source replication
Source-replica replication
Based on Raft distributed consensus protocol, minimum 3 replicas for continuous availability
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate ConsistencyStrong consistency on writes and tunable consistency on reads
Foreign keys infoReferential integritynonoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDDistributed ACID with Serializable & Snapshot Isolation. Inspired by Google Spanner architecture.
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infobased on RocksDB
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesno
User concepts infoAccess controlno infoCan implement custom security layer via applicationsnofine grained access rights according to SQL-standardyes
More information provided by the system vendor
FatDBSpark SQLTeradataYugabyteDB
Specific characteristicsYugabyteDB is an open source distributed SQL database for cloud native transactional...
» more
Competitive advantagesPostgreSQL compatible: Get instantly productive with a PostgreSQL compatible RDBMS....
» more
Typical application scenariosSystems of record and engagement for cloud native applications that require resilience,...
» more
Market metrics900,000+ lifetime clusters deployed 5.5K+ GitHub stars 4.3K+ YugabyteDB Community...
» more
Licensing and pricing modelsApache 2.0 license for the database
» more

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3rd partiesSQLFlow: Provides a visual representation of the overall flow of data. Automated SQL data lineage analysis across Databases, ETL, Business Intelligence, Cloud and Hadoop environments by parsing SQL Script and stored procedure.
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SQLFlow: Provides a visual representation of the overall flow of data. Automated SQL data lineage analysis across Databases, ETL, Business Intelligence, Cloud and Hadoop environments by parsing SQL Script and stored procedure.
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More resources
FatDBSpark SQLTeradataYugabyteDB
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