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Supported data models (conceptual, logical, physical) Supported notations Forward engineering Reverse engineering Model/database comparison and synchronization Teamwork/repository Database Workbench: Conceptual, logical, physical IE (Crow’s foot) Yes Yes Update database and/or update model No Enterprise Architect
It was founded in 2012 in Amsterdam, the Netherlands, and was previously known as Elasticsearch. [ 3 ] The company develops the Elastic Stack— Elasticsearch , Kibana , Beats, and Logstash—previously known as the ELK Stack, [ 4 ] free and paid proprietary features (formerly called X-Pack), Elastic Cloud (a family of SaaS offerings including ...
The combination of Elasticsearch, Logstash, and Kibana, referred to as the "Elastic Stack" (formerly the "ELK stack"), is available as a product or service. [6] Logstash provides an input stream to Elasticsearch for storage and search, and Kibana accesses the data for visualizations such as dashboards. [ 7 ]
Elasticsearch is a search engine based on Apache Lucene. It provides a distributed, multitenant -capable full-text search engine with an HTTP web interface and schema-free JSON documents. Official clients are available in Java , [ 2 ] .NET [ 3 ] ( C# ), PHP , [ 4 ] Python , [ 5 ] Ruby [ 6 ] and many other languages. [ 7 ]
The ANSI/SPARC three level architecture. This shows that a data model can be an external model (or view), a conceptual model, or a physical model. This is not the only way to look at data models, but it is a useful way, particularly when comparing models. [1] In 1975 ANSI described three kinds of data-model instance: [5]
Key–value (KV) stores use the associative array (also called a map or dictionary) as their fundamental data model. In this model, data is represented as a collection of key–value pairs, such that each possible key appears at most once in the collection. [26] [27] The key–value model is one of the simplest non-trivial data models, and ...
OLAP clients include many spreadsheet programs like Excel, web application, SQL, dashboard tools, etc. Many clients support interactive data exploration where users select dimensions and measures of interest. Some dimensions are used as filters (for slicing and dicing the data) while others are selected as the axes of a pivot table or pivot chart.
Data-driven models encompass a wide range of techniques and methodologies that aim to intelligently process and analyse large datasets. Examples include fuzzy logic, fuzzy and rough sets for handling uncertainty, [3] neural networks for approximating functions, [4] global optimization and evolutionary computing, [5] statistical learning theory, [6] and Bayesian methods. [7]