To the neophyte the world of technology seems bewildering. But – taken a step at a time – each part of the world of technology actually makes sense. The trick is to address technology a single step at a time. This course contains descriptions of all of the basics that a person not familiar with technology should know. This course is a good starting point for the person unfamiliar with technology. These segments are part of this video:
Indirect Access . This segment covers indirect access of data including the differences between searching for data and analyzing data. In explaining data analysis, we dive into textual analysis including patterns, classes of data, proximity of data, and summarizations. We discuss the benefits and challenges of searching versus analyzing text. We discuss the value of external categories.
DSS Environment. This segment covers the Decision Support Services (DSS) environment including a detailed explanation of the Corporate Information Factory (CIF). The different components of the CIF are defined including data marts, DSS applications, and the data warehouse. The importance of having a single source to which all data can be traced is emphasized. Different types of data marts are explored, including multi-dimensional Online Analytical Processing (OLAP), which is often called “cube” technology. Both drill down and drill up processing is explained with examples. This segment ends with a description of star joins.
Enterprise Resource Planning. This segment covers Enterprise Resource Planning (ERP) and the major players in this space including SAP. The drivers for implementing an ERP system are explained, as well as the pros and cons of buying versus building (buy vs. build).
Architecture Factors . This segment covers the factors that shape architecture, including capability, cost, efficiency, and demand.
Historical Perspective. This segment explains how we arrived at our current system state, in areas such as applications, databases, extract programs, personal computers, spreadsheets, data warehousing, and analytics. Difference data warehouse environments are explored including federated (sometimes called a “virtual data warehouse”) and the spider web environment.
Methodology. This segment covers the steps needed when building a data warehouse including starting with the data model, then designing the data warehouse, mapping to the legacy systems including order of development, and finally populating the data. Different development approaches are covered including the Software Development Lifecycle (SDLC) and agile/spiral approaches.
Parallel Processing. This segment covers parallel processing. There are many different ways to configure a computer to improve performance. What happens when either the workload or the volume of data grows? We talk about different solutions including increasing processor size and then introduce parallel processing. We discuss different ways that data can be spread across different processors.
Path Queue. This segment covers path queue for both structured and unstructured data. Textual extract, transform, and load (Textual ETL) is also discussed.
Structured Processing. We explain structured processing in this segment, including different approaches taken over time. We discuss structured programming including data flow diagrams and CRUD matrices, structured analysis, and standardized methodology such as the Software Development Lifecycle (SDLC).
Data Warehouse Monitor. This segment explains the data warehouse monitor. The monitor keeps track of data usage and identifies which data is dormant.
Transaction Processing. This video will explain transaction processing. We explore every step from when a user enters data via the keyboard to when the database returns data to the application for display back to the user.
Patterns of Access. This segment covers the different ways data can be organized on disk. We explore hashing algorithms and sequential approaches. We talk about the different patterns of access by users such as online and random.
Back Up and Recovery. This segment covers backup and recovery. We talk about the importance of backup, including legal and regulatory requirements. We cover change data capture and inexpensive backup options including log tapes.
Batch and Online. This segment explains both batch and online transactions. We talk about the versatility of a transaction, and the importance of a reader.
Online Transaction Processing.
Processor Architecture. This segment explains Online Transaction Processing (OLTP). The OLTP environment is discussed, as well as the OLTP monitor.
Tuning. This segment explains the importance of tuning as well as tuning approaches such as reconfiguration and reprioritization.
Storage. This segment explores storage along with its many variations, including historical storage. Learn about paper tape, punched cards, magnetic tape, and disk storage. Learn about the different parts of storage including keys, records, tracks, sectors, oxide, and disk.
System Components. This segment explains each computer component including processor, main memory, keyboard, disk storage, communications manager, applications, Database Management System (DBMS), Operating System (OS), internal wiring, and the monitor.
Standard Key Structure. This segment covers the various types of keys that can exist in our systems, and the pros and cons of each. We explore surrogate (or blind) keys as well as business keys. We discuss key conversion, including hashing algorithms.
Performance. This segment explores system performance. In the past, good performance was nice to have. Today good performance is mandatory. Many data warehouses today are mission critical and require quick response times. We discuss the factors impacting performance including data volumes, number of users, complexity of queries, and changing queries over time. We explain the Service Level Agreement (SLA) along with its importance.
Using Analysis Tools. This segment explains which analytical tools to use for different parts of the software development process including for requirements gathering, functional decomposition, Gantt charts, data flow diagrams, and HIPO.
Volumes of Data. This segment explores the challenges faced with large volumes of data. Learn how long data should exist within your environment, and plan to remove it after this time. We explain that data in a data warehouse can exist for very long periods of time, sometimes as long as ten years. We explore the issues on storage costs, performance, and administrative costs associated with storing large volumes of data. We discuss alternate forms of storage.