ASME STB-1-2020 pdf download.Guideline on Big Data/Digital Transformation Workflows and Applications for the Oil and Gas Industry.
2.3.2 Respective Databases (a) Data Lakes Also known as data warehouses or data swamps, data lakes are large repositories of data that are unstructured, or sometimes referred to as “raw.” Due to the storage requirements, most data lakes are stored in the cloud unless the organization owning the data has considerable onsite storage. Examples of off-site data lakes are Google 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 (IoT). (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 ODBMS, Key-value stores, Document stores, and Graph 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 DynamoDB, 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 elements 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 are suited for data sets with a wide variety of both structured and unstructured data. Examples of Graph databases include AllegroGraph, Neoj4, and Infinite Graph. These databases use languages to manage the data such as SPARQL, Java, and CYPHER.
2.4.3 Data Protection Big Data requires the processing and storage of large amounts of data. To process that data efficiently, it should ideally be available in unencrypted form. The obvious drawback is that hackers can steal or corrupt the data, or internal users can accidentally modify, destroy or corrupt data. An example: A central Contracts and Procurement organization identified that the company could procure less expensive pump seals than was being currently procured. The CP organization decided to implement the change for seals that had different materials of construction than what had been identified in the bills of materials (BOM) and material masters (MM) in a refinery’s Enterprise Resource Planning (ERP) system. The CP organization made changes to the refinery’s data systems so the lower cost seals would be purchased in the future. Unfortunately, the CP personnel neither used an approved MOC process to make changes nor engaged the discipline engineers at the refinery responsible for asset integrity and safety of the refinery. Several months later, site personnel discovered the change in the BOMs and Material Masters. The new seal materials identified by the CP organization were incompatible with the process fluids, which would have eventually resulted in a catastrophic failure of the seals and possibly fire and personnel injuries.ASME STB-1 pdf download.