Технические документы

Businesses rely on data for important decision-making, but may need to justify the Return on Investment (ROI) of data modeling within their organization. In this whitepaper, Tom Haughey discusses how to calculate the return on investment (ROI) of data modeling by assessing the economic value of real data modeling benefits, such as improved requirements definition, reduced maintenance, accelerated development, improved data quality and reuse of existing data assets.

Migrating from one data modeling tool to another doesn’t have to be difficult or painful. Use this migration guide to take you through the steps for importing your ERwin data models into ER/Studio.

Corporate data is dramatically increasing in importance. It is imperative to have trustworthy data that correctly represents all aspects of the business environment. Data modeling can be a useful way to improve data quality and integrity and establish the foundation for a business-driven data architecture. This Dimensional Research report contains a summary of survey results from 402 business and data stakeholders, covering current trends in corporate data and data modeling.

The data warehouse is an established concept and discipline that is discussed in many books, conferences and seminars. Into this world comes a new technology – Big Data. In this paper, William Inmon describes the differences and similarities between data warehouse architectures and Big Data technologies.

In this paper, Claudia Imhoff, Ph.D., describes an analytical architecture that expands on the existing Enterprise Data Warehouse (EDW) to include new data sources, storage mechanisms, and data handling techniques needed to support both conventional sources of data and those supplying Big Data.

This DATAVERSITY report consolidates the findings of their NoSQL survey, which examined a number of crucial issues within the NoSQL world today, with a focus on data modeling in particular.

Is your organization working on Agile or Scrum development projects? Many companies are moving to these methodologies for applications and databases, but haven’t taken them all the way back to the data models. In this whitepaper, Karen Lopez provides 10 tips and discusses ways for data modelers to work with sprint-focused agile development projects.

This analyst report examines the growing importance of model-driven processes for database design and management. The paper specifically focuses on the role and common use cases for business-driven metadata in database modeling, design, and development tools and how Embarcadero’s ER/Studio solution addresses the needs of today’s enterprise data landscapes.

Whether you’re working with relational data, schema-less data, or model metadata, you face challenges in providing the right access and context for your enterprise data assets. Read this eBook for essential insights on important data architecture topics.

Data architects face many challenges on a day-to-day basis which have made data modeling and metadata management more important than ever. The models and associated metadata are the only means by which complex data environments can truly be understood and managed. This whitepaper highlights five major challenge areas, and provide insights regarding how to address them with data modeling.