White Papers Plus

Introducing Teradata Columnar

The unique innovation by Teradata, in Teradata 14, is to add columnar structure to a table, effectively mixing row structure, column structure and multi-column structure directly in the DBMS which already powers many of the largest data warehouses in the world. With intelligent exploitation of Teradata Columnar in Teradata 14k, there is no longer the need to go outside the data warehouse DBMS for the power of performance that columnar provides, and it is no longer necessary to sacrifice robustness and support in the DBMS that holds the post-operational data.

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Starting Small,But Thinking Large And Scaling Fast (Teradata Data Warehouse Appliances)

As companies take steps to manage their information asset, choosing a platform and database management system (DBMS) is absolutely fundamental. In fact, the platform is the foundation of architecture and business intelligence and the starting point for tool selection, consultancy hires, and more. In short, a company’s platform is key in defining its information culture.

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Best Practices in the Use of Columnar Databases

Columnar databases are becoming an essential component of an enterprise infrastructure for the storage of data designed to run specific workloads. When an organization embraces the value of performance, it must do everything it can to remove barriers to the delivery of the right information at the right time to the right people and systems. There is no “ERP” for post-operational data. No one-size-fits-all system. Some gave that role to the relational, row-based data warehouse, but that ship has sailed. In addition to columnar databases, very-large data stores like Hadoop, real-time stream processing, and data virtualization are required today to bring together result sets across all data systems. This paper focuses on conveying an understanding of columnar databases and the proper utilization of columnar databases within the enterprise.

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Strategic Information Management Technology: Workloads Matter in Managing Gigabytes to Petabytes

This white paper is intended to provide a consolidated starting point for information technology managers who need to select systems to store retrievable analytic data for their business. The paper covers recommended use of information stores including relational row-based data warehouses and marts, multi-dimensional databases, columnar databases and MapReduce.

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Data Driven Design for Data Warehousing

In the data modeling area, rather than starting out with  the  grandiose goal of building the enterprise data model, as if it were a respectable end in itself, to be successful, data warehouse teams must leave the spotlight firmly on the business deliverables.  The data model, being a means to an end, is grounded in reality and constructed through a series of iterative progressions, staying in synch and not ahead of the partner components.

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The CIOs Guide to NoSQL

Co-authored with Dan McCreary. NoSQL is a new and fast-growing category of data management technologies that uses non-relational database architectures (hence NoSQL, or Not-Only SQL). NoSQL is not the best solution for every data management requirement, however it is often better suited to handle the requirements of high-performance, web-scalable systems and big data analysis. Organizations like Facebook, Twitter, Netflix and Yahoo are notable examples of innovators which have used NoSQL solutions to gain greater scale and performance, and at a fraction of the cost of traditional relational database systems.

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Making Information Management the Foundation of the Future (Master Data Management)

More complex and demanding business environments lead to more heterogeneous systems environments. This, in turn, results in requirements to synchronize master data. Master Data Management (MDM) is an essential discipline to get a single, consistent view of an enterprise’s core business entities – customers, products, suppliers, and employees. MDM solutions enable enterprise-wide master data synchronization. Given that effective master data for any subject area requires input from multiple applications and business units, enterprise master data needs a formal management system. Business approval, business process change, and capture of master data at optimal, early points in the data lifecycle are essential to achieving true enterprise master data.

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Mobile Business Intelligence: When Mobility Matters

Mobile business intelligence is a process, not a project, and a journey rather than a destination. The case studies included represent two forms that mobile business intelligence can take to empower the mobile worker and port existing applications.  This paper discusses two different companies, their environments, reasons for going mobile, and key success factors. The examples provide a framework of information architecture evaluation reference points, lay out options for mobile business intelligence, and provide best practices for those considering, planning, or doing some form of mobile business intelligence evaluation.

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Analyzing the Potential of the Contact Center Data Mart

Customer contact center data contains hidden nuggets of insight about customers, products, and business operations, and it provides the foundation for effective customer relationship management (CRM). Mining this data for insights can be daunting, however.

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Data Mart Consolidation: Repenting for Sins of the Past

This paper details the process of DMC at eight different organizations while capturing the keys to success from each. These case studies were specifically selected to demonstrate several variations on the concept of consolidation. While there is no such thing as a cookie-cutter DMC process, there are common best practices and lessons to be shared.

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