ASME STB-1:2020 pdf free download

ASME STB-1:2020 pdf free download.Guideline on Big Data/Digital Transformation Work flows and Applications for the Oil and Gas Industry.
Data mining technique. for unstructured data require tools that can process natural language andJor recognize patterns, and large quantities of both. In this sense, the data does contain a form of structure, but it is not casily recognizable.
2.3.2 Respective Databases
ta) Data 1.akcs
Also known as data warehouses or data swamps. data lakes arc large repositorics of data that arc unstructured, or sometimes referred to as “raw” Duc to the storage requirements. most data lakes arc stored in the cloud unless the organization owning the data has considerable onsite storage.
Examples of off-site data lakes are (]oogle Cloud. Amazon S3 or Apache Hadoop. A Hadoop is an open-source software for reliable, scalable and distributed computing. It provides massive storage capabilities and impressive computing power. It is not a programming language but rather an ecosystem that facilitates the moving and organization of Big Data, Hadoop-powered storage provides the capability for storing information derived by Internet of Things (loT).
(b) NoSQL
NoSQL databases, originally referred as “non-SQL” or “non-relational” database, store data in non- tabular form Included in this set of databases are the previously discussed Ol)BMS. Key-value stores. Document stores, and (1mph databases. Each of these types of databases uses a unique method to map data, documents, dictionaries or relationships through tags.
Examples of NoSQL include Apache Ignite. Couchbase. Oracle NoSQL. Amazon DynamoDli. and many others. These databases arrange data based on correlations of values rather than tables.
(c) Graph Databases
Graph databases are a unique subset of NoSQL databases that are gaining popularity for complex data mapping. They map data eleiiients on a chart or graph and have finite numbers of relations. Graph databases have nodes with data and edges that describe relationships. Each node can have many edges and therefore described many relationships. These databases arc suited for data sets with a wide variety of both structured and unstructured data.
Examples of Graph databases include AllcgroGraph. Ncoj4. and Infinitc Graph. These databases usc languages to manage the data such as SPARQL. Java, and CYPIIER.
2.4 Security and Governance of Data
2.4.1 Responsibility of the Enterprise
The enterprise team of engineers, planners, project managers and data professionals are responsible for solving business challenges using both internal and external sources of data. Many of these intemal sources are proprietary to the business and are key to the competitive advantage of the business. Some extemnal sources are subscription-based and should ideally be treated as confidential. The Data Scientist has a core responsibility to safeguard the confidentially provided information. Guarding this data requires both active protection (e.g. not sharing with other individuals not expressly governed by the same confidentiality) and
passive protection (e.g. following corporate protocols, using protection software, backing up data sets).