公司规模
Large Corporate
地区
- America
国家
- United States
产品
- Denodo Platform
- DI Classic
- DI Desktop
- Production Workspace application
- Royalty-info
技术栈
- Data Virtualization
- RESTful web services
- JDBC
- ETL
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Cost Savings
技术
- 平台即服务 (PaaS) - 数据管理平台
- 分析与建模 - 数据即服务
适用行业
- 石油和天然气
适用功能
- 产品研发
- 商业运营
用例
- 预测性维护
- 供应链可见性(SCV)
- 质量预测分析
服务
- 数据科学服务
- 系统集成
关于客户
Enverus is the leading SaaS and data analytics company for energy exploration decision support, helping the oil and gas industry achieve better, faster results. The company’s predictive decision platform combines intelligence, analytics, tools, and services in one seamless system to deliver value at every stage of the E&P process. Enverus services more than 3,200 companies globally from its Austin, Texas-based headquarters, and has more than 500 employees on five continents. Enverus’s primary goal is to enable key O&G market segments with information that drives business intelligence in these functions: decide where/how to drill and produce wells to generate the highest return (E&P companies); decide where and in whom to invest equity and debt financing (financial markets and M&A); and decide the highest potential business development opportunities (oil field services). To help drive these decisions, Enverus collects data from various sources and packages them up for sale to their customers.
挑战
Enverus’s business growth drove the need for the company to build next generation products to support key O&G market segments. These products include applications to support well production and oil field services workflows, geo services for map analysis, a Geology, Geophysical and Engineering (GG&E) platform for interpretations and visualization, as well as a soon to be released mineral interest analysis application. Rapid time-to-market for these products and applications was crucial and this implied that the Data Tech team needed to deliver a data platform that supported the internal application development team quicker than they had been doing in the past. Also, rapid delivery of data directly to the customers was needed as well. However, the Data Tech team was challenged with integrating the data across the data warehouse, other data sources and providing it to the data consumers quickly. The product development team’s delivery timelines were routinely at risk due to data availability and data consistency issues. As a result, the developers were directly accessing the data sources and in other cases suffering from severe delivery delays. To solve this issue, one option was to use conventional ETL (extract, transform, and load), but that would take several weeks. A more timely way of meeting the needs of the product development team was needed.
解决方案
Due to strength of the Denodo Platform to rapidly expose underlying data from source systems as data services, Enverus decided to use the solution to manage and quickly provision all of the data to the product development team and its customers. Enverus ETLs the regulatory agencies data into an internal data store that powers the DI Classic product. The production data is stored in another system called DI Desktop. The data from DI Classic, DI Desktop, as well as geo-spatial and Optical Character Recognition (OCR) data stored in other systems are then ETLd into their data warehouse. Enverus has created a virtual data abstraction layer using the Denodo Platform above the DI Classic, DI Desktop, and data warehouse. The Denodo Platform connects to these data sources, combines the data and publishes the resultant virtual views as data services, which are consumed internally by the application development team, analytics and decision support applications, and application data marts, as well as externally by their customers. Enverus uses Denodo’s caching mechanism to store data about business entities such as wells, completions, producing entities, permits, and so on, which are exposed as data services, analytics services, and map services. These services are then used by their internal application developers as well as customers to build applications.
运营影响
数量效益
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