Construction content of digital twin data lake for oil and gas fields
The construction of data lake includes the following points:
1) Data lake technology
The data lake technology architecture covers ten key areas, including data access (transfer), data storage, data computing, data application, data governance, metadata, data quality, data resource directory, data security and data audit. These ten areas work together to build a complete, efficient and secure data lake ecosystem, providing a solid foundation for enterprise data management and value mining.
Data access (mobile)
The data extraction link can obtain data from complex data sources through various advanced connectors and load it smoothly into the data lake. Whether it is structured data stored in traditional relational databases, semi-structured data in XML and JSON formats, or unstructured data such as text, images, and videos, they can all be effectively collected. The batch ingestion method that supports batch processing of large-scale data can meet the needs of enterprises for regular massive data updates; real-time ingestion ensures that enterprises can process real-time data such as financial transactions and IoT sensor data in real time without missing any key information; one-time load ingestion is suitable for data migration work in specific scenarios. In order to meet the diverse data source needs of enterprises, it is necessary to provide an adaptive multi-source heterogeneous data resource access method, such as data extraction tools based on ETL (Extract, Transform, Load) technology, and technologies such as Kafka that support real-time data transmission, to build an unimpeded channel for data extraction and aggregation in the enterprise data lake.
Data storage
The data storage system must have strong scalability to cope with the exponential growth trend of enterprise data volume. It can flexibly increase storage capacity according to the actual needs of the enterprise, and will not significantly affect the access performance of data during the expansion process. At the same time, it provides cost-effective storage solutions, such as using low-cost, high-capacity storage media such as object storage, to reduce the storage cost of the enterprise while ensuring the long-term preservation of data. In addition, the storage system must support multiple data formats, including columnar storage formats such as Parquet and ORC, as well as common formats such as CSV and JSON, to facilitate different types of data storage and subsequent processing, and meet the needs of fast access to data for exploration. Whether it is in-depth data analysis conducted by data scientists or simple data queries conducted by business personnel, the required data can be quickly obtained.
Data computing
The data lake needs to be equipped with a variety of data analysis engines with different functions to meet different computing scenarios. For batch processing of large-scale data, such as the generation of monthly financial statements and annual sales data analysis, batch computing engines such as Hadoop MapReduce can be used to efficiently process massive data; in real-time computing scenarios, such as real-time monitoring of stock price trends and real-time order processing of e-commerce platforms, real-time computing engines such as Flink can quickly process and analyze real-time data; and for streaming data processing, such as continuous data streams generated by IoT devices, streaming computing engines such as Spark Streaming can play their advantages. In order to meet the needs of high-concurrency reading and improve the efficiency of real-time analysis, the data lake must also have the ability to access massive data, and through distributed storage and caching technology, ensure that it can still respond quickly when a large number of users request data at the same time. In addition, it must be compatible with various open source data formats, such as Parquet and ORC mentioned above, and can directly access data stored in these formats without complex data format conversion, which greatly improves the efficiency of data processing.
Data Governance
Data governance is the core work throughout the entire life cycle of the data lake, aiming to manage the availability, security and integrity of data in the data lake. By formulating a clear and specific data governance strategy, we can point out the direction for the data management work of the enterprise; establish a sound data governance framework, clarify the responsibilities and authority of each department in data management; formulate detailed data management policies, and standardize each link such as data collection, storage, and use. Realize data sharing, break the data silos within the enterprise, and enable different departments to efficiently obtain and use data. For example, by establishing a data standard system, we can ensure that the definition and understanding of the same data are consistent among various departments within the enterprise; through the data quality management process, we can ensure the accuracy and integrity of the data; through the data security management mechanism, we can prevent data leakage and illegal access. Data governance provides comprehensive guidance and strict supervision for all other data management functions, and is the key to ensuring that the data lake can operate continuously, stably, and efficiently.
Metadata
Metadata management is the basic work of data lake construction, which runs through the entire life cycle of data. Metadata is like the "instructions" of data, recording key information such as the definition, source, format, and update frequency of data. Enterprises need to manage the life cycle of metadata in a refined manner, from the generation and collection of metadata to storage, use, and finally update and archiving, there must be clear processes and specifications. Metadata management itself is not the ultimate goal, but an important means for organizations to mine more value from their data. Through effective management of metadata, enterprises can discover and understand data more quickly and improve the efficiency of data use. For example, when conducting data analysis, data analysts can quickly understand the structure and meaning of data through metadata and choose appropriate analysis methods; in the process of data integration, developers can accurately connect and convert data based on metadata. To achieve a data-driven enterprise operation model, it is first necessary to achieve metadata-driven, making metadata the core driving force of enterprise data management and value mining.
Data Resource Directory
The initial construction of the data resource directory is a complex and important task, which usually requires scanning a large amount of data to comprehensively collect metadata. The data it covers includes data assets that are identified as valuable and shareable in the data lake, which may come from various business systems, databases, and external data sources of the enterprise. With the help of advanced algorithms and machine learning technologies, the data resource directory can automatically complete a series of key tasks. Through intelligent search and scanning of data sets, the required data can be quickly located; metadata can be extracted to support the discovery and understanding of data sets; in the process of data integration, data conflicts can be keenly exposed, such as inconsistent definitions of the same data field in different data sources; semantic analysis and machine learning models can be used to infer semantics and business terms to make the meaning of data clearer and easier to understand; data can be labeled to facilitate users to quickly find the required data through search functions; at the same time, privacy, security and compliance of sensitive data can be accurately identified to ensure that the use of data complies with relevant laws and regulations and internal corporate security policies.
Privacy and Security
Data security is a crucial link in the construction of data lakes, involving the careful planning, comprehensive development and strict implementation of security policies and security procedures. Its purpose is to provide reliable authentication, authorization, access control and auditing functions for data and information assets. At every level of the data lake, from the underlying data storage to the data discovery and upper-level data consumption, all-round security protection must be achieved. The most basic requirement is to effectively prevent unauthorized users from accessing and prevent data leakage and malicious attacks. Through authentication mechanisms, such as username and password verification and multi-factor authentication, it is ensured that only legitimate users can log in to the system; the audit function records all user operations so that they can be traced and analyzed when problems arise; the authorization mechanism clarifies the access rights of different users to data. For example, ordinary business personnel can only view data within a specific range, while data administrators have higher permissions; data protection technologies, such as data encryption and data desensitization, encrypt the storage and transmission of sensitive data, and desensitize sensitive information during data use to ensure data security. Authentication, auditing, authorization, and data protection functions work together to form a solid line of defense for data lake security.
Data quality
Data quality is a core component of the data lake architecture and is directly related to the commercial value of data. High-quality data can provide enterprises with accurate and reliable insights, while information extracted from poor-quality data often leads to wrong decisions and analysis results. Data quality focuses on the implementation capabilities of demand analysis, inspection, analysis, and improvement. In the planning stage of data, data quality requirements and standards are clearly defined; when acquiring data, the source of data is strictly screened and verified; during the storage process, data cleaning and verification technology is used to ensure the accuracy and integrity of data; when sharing data, the consistency of data is monitored; when maintaining data, erroneous data is updated and repaired in a timely manner; when applying data, the validity of data is evaluated; in the data extinction stage, useless data is safely cleaned up. Through a series of activities such as comprehensive identification, accurate measurement, real-time monitoring and timely warning of various data quality problems that may arise in each stage of the entire life cycle of data from planning, acquisition, storage, sharing, maintenance, application, and extinction, and through continuous improvement and improvement of the management level of the organization, optimization of data management processes, and strengthening of personnel training, data quality is further improved.
Data Audit
Data audit mainly undertakes two key tasks. One is to track changes to key data sets to ensure that any changes to important data can be recorded and monitored in a timely manner. The second is to capture the changes of important data set elements in detail, including how to make changes (such as the specific content of the modification, the operation method), when to make changes, and the personnel information of these elements. By recording and analyzing this information, data auditing can provide enterprises with powerful risk assessment and compliance inspection support. For example, in the financial industry, regulators require enterprises to conduct strict audits on changes in customer transaction data to ensure the compliance of transactions and the security of data; within the enterprise, data auditing can help discover potential data security vulnerabilities and illegal operations, take timely measures to prevent and correct them, and reduce the operational risks of the enterprise.
Data application
Data application is a key link in realizing the value of the data lake. Through unified management, in-depth processing and extensive application of the data in the data lake, it provides all-round support for the internal and external businesses of the enterprise. Within the enterprise, it supports all aspects of business operations, such as optimizing sales strategies through the analysis of sales data; optimizing processes and improving production efficiency through monitoring and analysis of production data; using customer data for precise marketing promotion to improve customer satisfaction and loyalty; evaluating and warning various risks faced by the enterprise through risk management models; integrating data from different channels to achieve channel integration and improve the overall operational efficiency of the enterprise. Outside the enterprise, support open data sharing, share valuable data with partners, and achieve mutual benefit and win-win results; provide data services to transform data into commercial value, such as providing data analysis reports and data mining services to other enterprises. In addition to basic computing capabilities, the data lake needs to provide a rich variety of upper-level applications. The batch report function can regularly generate various reports required by the enterprise, such as financial reports, sales reports, etc.; ad hoc queries allow users to query data at any time according to their needs and quickly obtain the required information; interactive analysis provides a visual analysis interface, allowing users to perform data analysis through dragging, clicking, and other operations; the data warehouse function integrates and stores the company's historical data to provide support for in-depth analysis; the machine learning function uses a large amount of data in the data lake for model training to achieve intelligent prediction and decision support. In addition, it is also necessary to provide self-service data exploration capabilities so that business personnel can independently explore and analyze data without relying on professional data analysts and discover the potential value in the data.
2) Data service technology
Based on the data asset layer, the data service layer will focus on building the two core modules of "data box" and "toolbox". Through the careful construction of different levels of detailed components in these two key modules, it provides strong support for the continuous innovation of the data front-end and significantly improves the data analysis capabilities of enterprises.
Data Box
The data box provides data services derived from the data asset layer. Its core is the scenario-based data collection, data-based calculation indicators, and scenario tags based on data applications. This enables data to be organized and presented in a way that is closer to business needs, providing strong support for the business decisions of enterprises.
Data: Covers the full-domain query function of data assets. Enterprise users can quickly retrieve data assets distributed in every corner of the data lake through a unified query interface. At the same time, the data assets are re-integrated in business scenarios, and the data originally scattered in different business systems are recombined and sorted according to specific business scenarios to facilitate scenario applications and system calls. For example, in the promotional activities of e-commerce companies, user purchase data, product inventory data, marketing promotion data, etc. are integrated to provide comprehensive data support for event planning and execution.
Tags: Provide powerful capabilities for convenient search of data assets at the business level, becoming a key "handle" for the precipitation of business value. The tag library in the toolbox is uniquely designed and is divided into a public tag library and a personal tag library. The public tag library provides a unified tag system for all departments of the enterprise, which facilitates convenient data search at the business level and ensures that different personnel have the same understanding and search methods for the same data. The personal tag library provides a channel for business personnel to mark unique business values of data assets according to their own business needs, forming personalized business value precipitation. For example, employees in the marketing department can add personalized tags to customer data based on customer purchasing behavior and preferences for precision marketing.
Indicators: As an important module in the data box, the unified caliber of the indicator module is its necessary attribute and basic principle. The indicator module design is divided into two major sections. The [Indicator Master Library] is responsible for unifying the indicator calculation logic to ensure that the calculation method of the same indicator is consistent in different business scenarios. For example, for the indicator "sales", whether it is calculated in the sales department, the finance department or the marketing department, the same calculation logic is followed. The [Indicator Library] unifies the indicator data acquisition logic, clarifies which data sources are used to obtain data for indicator calculation, and ensures the accuracy and consistency of the data. These two major sections jointly support the data service of the indicator module, providing enterprises with reliable indicator data for business analysis and decision-making.
Model: The model library is a result-oriented tool component that is divided by scenario and can be directly used for application. In the design of the model library, the division of model types is closely based on the application requirements of the front, middle and back ends of the business for the model. The front-end business model may focus on user behavior analysis and marketing recommendations, such as a product recommendation model based on user browsing history; the middle-end business model pays more attention to business process optimization and risk management, such as supply chain optimization models and credit risk assessment models; the back-end business model mainly serves the company's resource management and strategic decision-making, such as financial forecasting models and human resource planning models. The model library covers the entire operational process information of the model from generation, application to management, including the model's training data, algorithm parameters, evaluation indicators, etc., to ensure the effectiveness and maintainability of the model.
Toolbox
Visualization component: "A picture is worth a thousand words". The visualization component uses various charts, such as bar charts, line charts, pie charts, heat maps, etc. to present complex data in an intuitive and easy-to-understand way.
This clear communication method enables business personnel to understand and process the information they face more quickly, transforming "cold" data into vivid charts, greatly reducing the threshold for data understanding. At the same time, the componentized design method provides strong support for data analysis innovation. Users can flexibly select and combine different visualization components according to their needs to create visualization reports that meet their own analysis ideas. For example, in sales data analysis, the sales comparison of different regions is displayed through a bar chart, and the sales trend over time is displayed through a line chart, which helps business personnel quickly discover the rules and problems in sales data.
Cognitive service: Precipitate cutting-edge cognitive service capabilities to provide strong support for front-end business systems. In terms of user identity verification, advanced biometric technologies such as face recognition and fingerprint recognition, as well as behavioral recognition technology based on big data analysis, are used to ensure the authenticity and security of user identities. In terms of risk prevention and control, machine learning and artificial intelligence algorithms are used to conduct real-time analysis of user behavior data, transaction data, etc., to promptly discover potential risk behaviors, such as fraudulent transactions, malicious attacks, etc., and take corresponding prevention and control measures. For example, in financial transaction systems, users’ transaction behaviors can be monitored in real time through cognitive services. Once abnormal transactions are discovered, warnings and blocks are immediately issued to ensure the safety of users’ funds and the stability of business operations.