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DBMS > FatDB vs. TigerGraph vs. Vertica vs. XTDB

System Properties Comparison FatDB vs. TigerGraph vs. Vertica vs. XTDB

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Editorial information provided by DB-Engines
NameFatDB  Xexclude from comparisonTigerGraph  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparisonXTDB infoformerly named Crux  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.A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-timeCloud 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.A general purpose database with bitemporal SQL and Datalog and graph queries
Primary database modelDocument store
Key-value store
Graph DBMSRelational DBMS infoColumn orientedDocument store
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.83
Rank#139  Overall
#13  Graph DBMS
Score10.68
Rank#43  Overall
#27  Relational DBMS
Score0.11
Rank#343  Overall
#46  Document stores
Websitewww.tigergraph.comwww.vertica.comgithub.com/­xtdb/­xtdb
www.xtdb.com
Technical documentationdocs.tigergraph.comvertica.com/­documentationwww.xtdb.com/­docs
DeveloperFatCloudOpenText infopreviously Micro Focus and Hewlett PackardJuxt Ltd.
Initial release2012201720052019
Current release12.0.3, January 20231.19, September 2021
License infoCommercial or Open Sourcecommercialcommercialcommercial infoLimited community edition freeOpen Source infoMIT License
Cloud-based only infoOnly available as a cloud servicenonono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containersno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC#C++C++Clojure
Server operating systemsWindowsLinuxLinuxAll OS with a Java 8 (and higher) VM
Linux
Data schemeschema-freeyesYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.schema-free
Typing infopredefined data types such as float or dateyesyesyesyes, extensible-data-notation format
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 indexesyesNo Indexes Required. Different internal optimization strategy, but same functionality included.yes
SQL infoSupport of SQLno infoVia inetgration in SQL ServerSQL-like query language (GSQL)Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.limited SQL, making use of Apache Calcite
APIs and other access methods.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
HTTP REST
JDBC
Supported programming languagesC#C++
Java
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Clojure
Java
Server-side scripts infoStored proceduresyes infovia applicationsyesyes, PostgreSQL PL/pgSQL, with minor differencesno
Triggersyes infovia applicationsnoyes, called Custom Alertsno
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioning, hierarchical partitioningnone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.yes, each node contains all data
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyesno infoBi-directional Spark integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyes infoRelationships in graphsyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB
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 applicationsRole-based access controlfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
FatDBTigerGraphVertica infoOpenText™ Vertica™XTDB infoformerly named Crux
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
FatDBTigerGraphVertica infoOpenText™ Vertica™XTDB infoformerly named Crux
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