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DBMS > FatDB vs. Spark SQL vs. Vertica

System Properties Comparison FatDB vs. Spark SQL vs. Vertica

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Editorial information provided by DB-Engines
NameFatDB  Xexclude from comparisonSpark SQL  Xexclude from comparisonVertica infoOpenText™ Vertica™  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 processingCloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.
Primary database modelDocument store
Key-value store
Relational DBMSRelational DBMS infoColumn oriented
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score19.56
Rank#34  Overall
#21  Relational DBMS
Score11.76
Rank#44  Overall
#28  Relational DBMS
Websitespark.apache.org/­sqlwww.vertica.com
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmlvertica.com/­documentation
DeveloperFatCloudApache Software FoundationOpenText infopreviously Micro Focus and Hewlett Packard
Initial release201220142005
Current release3.5.0 ( 2.13), September 202312.0.3, January 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenonono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC#ScalaC++
Server operating systemsWindowsLinux
OS X
Windows
Linux
Data schemeschema-freeyesYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.
Typing infopredefined data types such as float or dateyesyesyes
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.nono
Secondary indexesyesnoNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLno infoVia inetgration in SQL ServerSQL-like DML and DDL statementsFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methods.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
JDBC
ODBC
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesC#Java
Python
R
Scala
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresyes infovia applicationsnoyes, PostgreSQL PL/pgSQL, with minor differences
Triggersyes infovia applicationsnoyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark Corehorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factornoneMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesno infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
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.nono
User concepts infoAccess controlno infoCan implement custom security layer via applicationsnofine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
FatDBSpark SQLVertica infoOpenText™ Vertica™
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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Competitive advantagesFast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
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More resources
FatDBSpark SQLVertica infoOpenText™ Vertica™
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