In Dragon1, you have access to a modern set of symbols for creating a data lake architecture diagram, but also a data warehouse or any artificial intelligence solution diagram.
You can make use of Amazon (AWS) symbols and create, for instance, a solution architecture for your Data Lake AWS, like the one below.
Below you see one of the many storage scenarios possible on Azure, the Microsoft Cloud Service.
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A data lake is a system or repository of data, where the data is stored in its original (raw) format. Usually, this is in the form of files.
Often a data lake is a single store of all enterprise data including raw copies of source system data and transformed data used for tasks such as reporting, visualization, advanced analytics and machine learning. It is one of the most important architectural concepts to make artificial intelligence happen.
Dragon1 supports you to work on the platform in a repository application and in a designer application. Dragon1 also supports you to work with .dragon1 Files.
Any XLSX or CSV file and any data in the Dragon1 repository can be converted into, imported and exported as .dragon1 Files. Below is an example screenshot of a .dragon1 File.
Data Lakes can contain structured data from relational databases (in rows and columns or object-oriented nodes) or semi-structured data (such as XML, JSON, CSV and logs) or any unstructured data (like PDFs, documents and email) and also binary data.
They are both widely used for the storage of big data, but they are not interchangeable. Lakes are often pools of data in the raw original format, the purpose for which is not yet defined. A data warehouse is more like a repository for structured and filtered data that has been processed for specific purposes.
The dynamic example is repeated below as a static diagram. It is an effective way of visualizing this concept. It is a solution reference architecture diagram.
Azure (from Microsoft) and AWS (from Amazon) are two well-known solutions that include all the capabilities required to make it easy for developers, data scientists, and analysts to store data of any size, shape, and speed, and do all types of processing and analytics across platforms and languages.
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