EXAM 1 Chapter 3 Study Guide – Flashcards

big data refers to everything that enables an organization to create, manipulate, and manage very large data sets (measured in terabytes, petabytes, exabytes, etc.) and the facilities in which these are stored. Distributed data centers, data warehouses, and cloud-based storage are common aspects of today's enterprise networks.

Many factors have contributed to the merging of "big data" and business networks including continuing declines in storage costs, the maturation of data mining and business intelligence (BI) tools, and government regulations and court cases that have caused organizations to stockpile large masses of structured and unstructured data including documents, e-mail messages, voice-mail messages, text messages, and social media data.

A data center can occupy a single room in a building, one or more floors, or an entire building. Much of the equipment inside a data center consists of servers mounted in rack cabinets that are placed in rows that form corridors (aisles) that enable access to both the front and the rear of each cabinet. Because mainframe computers and storage devices can be comparable in size to the rack cabinets, they are placed alongside the racks. To ensure proper performance of the servers and computing equipment, air-conditioning is used to control temperature and humidity in the data center.

In a centralized data processing architecture, data processing is done on one or a cluster of computers, generally powerful and/or physically large computers, located in a central data processing facility.

Centralized computers Centralized processing Centralized data Centralized control Centralized support staff

A centralized data processing architecture has a number of attractive aspects. There may be economies of scale in the purchase and operation of equipment and software. A centralized IT services organization is often better able to attract and retain the highly paid professional programmers needed to support enterprise-wide systems and/or the specialized needs of strategic business units. Data center managers can maintain control over data center hardware procurement, enforce standards for data quality, and design and implement appropriate security policies.

When a distributed data processing strategy is followed, computers (usually smaller computers) are dispersed throughout an organization. The objective of such dispersion is to process information in a way that is most effective based on operational, economic, and/or geographic considerations, or all three.

The objective of such dispersion is to process information in a way that is most effective based on operational, economic, and/or geographic considerations, or all three.

Tier 1 - No redundancy guarantees for servers, storage systems, network equipment, and communication connections to Internet or other networks. Single path for power and cooling distribution; no redundant components. Tier 2 - Meets or exceeds all Tier 1 requirements. Single, nonredundant connection to Internet and/or other networks. Redundant capacity components (servers, storage systems, network equipment). Includes raised floor, UPS (uninterruptible power supply), and on-site generator. Tier 3 - Meets or exceeds all Tier 2 requirements. Dual-power sources for all IT equipment. Multiple independent distribution links between IT equipment and network switches. Provides multiple (redundant) links to the Internet and/or other networks. Includes sufficient capacity to carry load on one distribution path while doing maintenance on the other. Tier 4 - Meets or exceeds all Tier 3 requirements. All components have redundant backups to ensure that they are fault-tolerant. This includes storage systems, HVAC (heating, ventilation, and air conditioning) systems, servers, etc. Includes on-site electrical power storage and distribution facilities. Typically includes compartmentalized security zones controlled by biometric access controls.

In-memory processors include terabyte-plus RAM capable of storing large data sets. By keeping data close to the processor, computations on big data sets can be performed much faster than is possible with traditional server processors where data has to be sent back and forth to storage devices because of limited RAM.

SAP's in-memory computing system is called HANA (High performance Analytic Appliance). It looks like other high-end servers that can be installed in regular-sized data center server racks, but is equipped with Intel in-memory processors [HARD12].

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