Analytics are forming the basis of competition today. This white paper addresses what distinguishes analytics and answers the question are we doing analytics in the data warehouse? The paper further talks about the contending Platforms for the Analytics Workload and introduces the ParAccel Analytic Database as a key component of Information Architecture.
This white paper provides guidance around information management data store category selection including the NoSQL movement and how to prepare for success in this new ecosystem – not the least of which is to have robust data integration capabilities for the heterogeneous environment. As one senior technology leader recently said to me: “There are a million architectures today for my data.” How true!
Communications companies are currently embroiled in a series of initiatives to enable the convergence of systems to deliver converged services – the utilization of a single network to transport all information and services (voice, data and video) by encapsulating the data into packets. Providing converged services is rendering inadequate the isolated systems that are in place in many of these companies. It does not take long in this systems evolution to be reminded of the immense importance of access to high-quality, well-performing and integrated information in support of the transition. Learn how the analytic database supports the needs of Converged Systems.
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.
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.
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.
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.
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.
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.
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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