CISB594 – Business Intelligence Data Warehousing Part II

Reference Materials used in this presentation are extracted mainly from the following texts, unless stated otherwise.

Objectives At the end of this lecture, you should be able to: Describe the processes used in developing and managing data warehouses Explain data integration and the extraction, transformation, and load (ETL) processes Describe real-time (active) data warehousing Understand data warehouse administration and security issues CISB594 – Business Intelligence

Data Integration: The Extraction, Transformation, and Load (ETL) Process Data integration is a term that covers three processes which combine to move data from multiple sources into a data warehouse:

Data Integration : The Extraction, Transformation, and Load (ETL) Process Extraction, transformation, and load (ETL) technologies Fundamentally, a DW could not exist without ETL The ETL process consists of extraction (reading data from one or more databases), transformation (converting the extracted data from its previous form into the form in which it needs to be so that it can be placed into a data warehouse) load (putting the data into the data warehouse) The ETL process also contributes to the quality of the data in a DW, its purpose is to load the data warehouse with integrated and cleansed data

Data Transformation Tools – To purchase or to build? The process can either be done through purchasing data transformation tools and setup ETL, or purchase the tool together with the expertise to setup ETL, or developing the tools using programming languages. Programmers can set up ETL processes using almost any programming language, but building such processes from scratch is very complex. (we are trying to talk to different sources here!)

Data Transformation Tools – To purchase or to build? Increasingly, companies are buying ETL tools to help in the creation of ETL process (Wikipedia) Examples of ETL tools IBM InfoSphere DataStage Microsoft SQL Server Integration Services (SSIS) Oracle Data Integrator (ODI)

Data Transformation Tools – To purchase or to build? A Consideration Should organization purchase data transformation tools or build the transformation process itself ? Data transformation tools are expensive Data transformation tools may have a long learning curve It is difficult to measure how the IT organization is doing until it has learned to use the data transformation tools Not an easy decision ! However many believe purchasing the tools should be able to kick start the project faster and simplify the maintenance of the data warehouse .

Data Warehouse Development Choosing the vendors Six guidelines to consider a vendor Financial strength Qualified consultants Market share Industry experience Established partnerships - These indicate that a vendor is likely to be in business for the long term, to have the support capabilities its customers need, and to provide products that interoperate with other products the potential user has or may obtain.

Data Warehouse Development A data warehousing project is a major undertaking MORE COMPLICATED AS IT COMPRISES AND INFLUENCES MANY DEPARTMENTS, INPUT OUTPUT INTERFACES AND CAN BE PART OF BUSINESS STRATEGY Data warehouse development approaches Inmon Model: EDW approach Kimball Model: Data mart approach Which model is best? There is no one-size-fits-all strategy to data warehousing It depends on the need and the capacity of the organization For many organizations, data mart approach is a convenient first step in implementing DW

Data Warehouse Development Kimball Model Inmon Model Pros: Easy to build organizationally Easy to build technologically Cons: Enterprise wide view unavailable Redundant data costs High ETL costs High DBA costs Business Enterprise View Design consistency Data reusability Require corporate leadership and vision

Eventually it can be this …

Data Warehouse Development Describe the major similarities and differences between the Inmon and Kimball data warehouse development approaches. Similarities: Both methods can produce an enterprise data warehouse and subset data marts. Differences: Inmon’s approach starts with an enterprise data warehouse, creating data marts as subsets of that EDW if appropriate. The focus is on proven, traditional methods and technologies. Kimball’s starts with data marts, consolidating them into an EDW later if appropriate. It focuses in creating a useful end-user capability quickly.

Data Warehouse Development Effort Data Mart (Kimball Model) EDW (Inmon Model) Scope One subject area Several subject areas Development Time Months Years Development Cost $10K - $100 K ++ $1000000 ++ Development Difficulty Low to medium High Sources Only some operational and external systems Many operational and external systems Size Megabytes to several gigabytes Gigabytes to petabytes Hardware Workstations and departmental servers Enterprise servers and mainframe OS Windows and Linux Unix, S/390 Number of simultaneous users 10s 100s to 1000s

Real-Time Data Warehousing Traditionally, a data warehouse are not business critical, data are commonly updated on a weekly basis – not allowing for responding to transactions in near real time Today, organizations are facing the need for real-time data warehousing, as decision support has become operational. The emergence of real-time data warehousing (RDW) or active data warehousing (ADW) The process of loading and providing data via a data warehouse as they become available

Real-Time Data Warehousing The need for real-time data A business often cannot afford to wait a whole day for its operational data to load into the data warehouse for analysis Real-time data collection can reduce or eliminate the nightly batch processes

Mistakes Starting with wrong sponsorship chain – You need the right champion, somebody influential over the sources and directions Setting unrealistic expectations – remember, BI initiative relies on management’s good decision making as much as it relies on the technology used. Engaging in political naïve behaviour – Don’t imply that managers have been making bad decisions without BI Loading DW with information just because it is available Believing that DW design is the same as transactional DB design. Choosing a DW admin who is only technology oriented.

Data Warehouse Administration and Security Issues Due to its huge size and complicated nature, DW requires strong monitoring and administrating Needs more than a DBA Needs data warehouse administrator (DWA) A person responsible for the administration and management of a data warehouse

Data Warehouse Administration and Security Issues What skills should a DWA possess? Why? Familiarity with high-performance hardware, software and networking technologies, since a data warehouse is based on those. Solid business insight, to understand the purpose of the DW and its business justification, familiarity with business decision, making processes to understand how the DW for business strategic purpose will be used easy. Excellent communication skills, to communicate with the rest of the organization

Data Warehouse Administration and Security Issues Effective security in a data warehouse should focus on four main areas: 1. Establishing effective corporate and security policies and procedures. An effective security policy should start at the top and be communicated to everyone in the organization. 2. Implementing logical security procedures and techniques to restrict access. This includes user authentication, access controls, and encryption. 3. Limiting physical access to the data center environment. 4. Establishing an effective internal control review process for security and privacy

Assignment question Discuss the architecture and data warehouse model approach – include diagram and justify the choice Discuss whether your team will recommend the organization to purchase tools from vendors or build the system itself. Justify your suggestion and include cost estimation.

Now ask if .. You are able to: Describe the processes used in developing and managing data warehouses Explain data integration and the extraction, transformation, and load (ETL) processes Describe real-time (active) data warehousing Understand data warehouse administration and security issues CISB594 – Business Intelligence