Chapter 4 Data Warehousing

Outline Definition of Data Warehouse Reasons for creating Data Marts Benefits and characteristics of Data Warehouse Reasons for need of data warehousing Operational and Informational Systems Data Warehouse vs Data Mart Types of Systems Used Data warehouse architectures List four steps of data reconciliation Design a data mart Star Schema

Definition of Data Warehouse It is a huge central database that accepts, stores and maintain data from different sources and locations. Disparate sources may use different formats and technologies.

Definition of Data Mart A data mart is a simple form of a data warehouse that is focused on a single subject (or functional area), such as sales, finance or marketing. Data marts are small slices of the data warehouse. Data marts are often built and controlled by a single department within an organization. Given their single-subject focus, data marts usually draw data from only a few sources. The sources could be internal operational systems, a central data warehouse, or external data.

Reasons for creating a data mart Easy access to frequently needed data Creates collective view by a group of users Improves end-user response time Ease of creation Lower cost than implementing a full data warehouse Potential users are more clearly defined than in a full data warehouse Contains only business essential data and is less cluttered.

Benefits of Data Warehouse Collect data from multiple sources into a single database so a single query engine can be used to present data. Maintain data history, even if the source transaction systems do not. Integrate data from multiple source systems, enabling a central view across the enterprise. Improve data quality by flagging or even fixing bad data. Present the organization's information consistently (constantly and reliably). Provide a single common data model for all data of interest regardless of the data's source. Restructure the data so that it makes sense to the business users. Making decision–support queries are easier to write.

Example of using a Data Warehouse

Characteristics of Data Warehouse A data warehouse is a system used for reporting and data analysis. Integrating data from one or more disparate sources creates a central repository of data, a data warehouse (DW). Data warehouses store current and historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons. The data stored in the warehouse is uploaded from the operational systems.

Operational and Informational Systems

Data Warehouse Versus Data Mart

Types of systems used (1) Online Analytical Processing (OLAP) It is characterized by a low volume of transactions. Queries are often very complex and involve aggregations. OLAP databases store aggregated, historical data in multi-dimensional schemas (usually star schemas). Online Transaction Processing (OLTP) Characterized by a large number of transactions (INSERT, UPDATE, DELETE). OLTP databases contain detailed and current data. The schema used to store transactional databases is the entity model (usually 3NF).

Types of systems used (2) Predictive analysis It is about finding and quantifying hidden patterns in the data using complex mathematical models that can be used to predict future outcomes. Predictive analysis is different from OLAP in that OLAP focuses on historical data analysis and is reactive in nature, while predictive analysis focuses on the future.

Data Warehouse Architectures Generic Two-Level Architecture Independent Data Mart Dependent Data Mart and Operational Data Store Logical Data Mart and Real-Time Data Warehouse Three-Layer architecture All involve some form of extraction, transformation and loading (ETL)

Generic two-level data warehousing architecture One, company-wide warehouse Periodic extraction  data is not completely current in warehouse

Data Warehousing Architecture Independent Data Mart Data Warehousing Architecture Data marts: Mini-warehouses, limited in scope E T L Separate ETL for each independent data mart Data access complexity due to multiple data marts

Dependent data mart with operational data store: a three-level architecture Single ETL for Enterprise Data Warehouse (EDW) Simpler data access ODS provides option for obtaining current data Dependent data marts loaded from EDW

Logical data mart and real time warehouse architecture Near real-time ETL for Data Warehouse ODS and data warehouse are one and the same Data marts are NOT separate databases, but logical views of the data warehouse  Easier to create new data marts

Three-layer data architecture for a data warehouse

Data Characteristics: Status vs. Event Data Event = a database action (create/update/delete) that results from a transaction

Data Characteristics: Transient vs. Periodic Data With transient data, changes to existing records are written over previous records, thus destroying the previous data content Transient operational data

Data Characteristics: Transient vs. Periodic Data Periodic data are never physically altered or deleted once they have been added to the store Periodic warehouse data

The Reconciled Data Layer Typical operational data is: Transient–not historical Not normalized (perhaps due to denormalization for performance) Restricted in scope–not comprehensive Sometimes poor quality–inconsistencies and errors After ETL, data should be: Detailed–not summarized yet Historical–periodic Normalized–3rd normal form or higher Comprehensive–enterprise-wide perspective Timely–data should be current enough to assist decision-making Quality controlled–accurate with full integrity

Scrub or data cleansing Transform Load and Index The ETL Process Capture/Extract Scrub or data cleansing Transform Load and Index ETL = Extract, transform, and load

Steps in data reconciliation (1) Capture/Extract…obtaining a snapshot of a chosen subset of the source data for loading into the data warehouse Static extract = capturing a snapshot of the source data at a point in time Incremental extract = capturing changes that have occurred since the last static extract

Steps in data reconciliation (2) Scrub/Cleanse…uses pattern recognition and AI techniques to upgrade data quality Fixing errors: misspellings, erroneous dates, incorrect field usage, mismatched addresses, missing data, duplicate data, inconsistencies Also: decoding, reformatting, time stamping, conversion, key generation, merging, error detection/logging, locating missing data

Steps in data reconciliation (3) Transform = convert data from format of operational system to format of data warehouse Record-level: Selection–data partitioning Joining–data combining Aggregation–data summarization Field-level: single-field–from one field to one field multi-field–from many fields to one, or one field to many

Steps in data reconciliation (4) Load/Index= place transformed data into the warehouse and create indexes Refresh mode: bulk rewriting of target data at periodic intervals Update mode: only changes in source data are written to data warehouse

Single-field transformation In general–some transformation function translates data from old form to new form Algorithmic transformation uses a formula or logical expression Table lookup–another approach, uses a separate table keyed by source record code

Multifield transformation M:1–from many source fields to one target field 1:M–from one source field to many target fields

The star schema separates business process data into facts. Facts hold the measurable, quantitative data about a business, and dimensions which are descriptive attributes related to fact data. Examples of fact data include sales price, sale quantity, and time, distance, speed, and weight measurements.

Components of a star schema Fact tables contain factual or quantitative data Dimension tables contain descriptions about the subjects of the business 1:N relationship between dimension tables and fact tables Excellent for ad-hoc queries, but bad for online transaction processing Dimension tables are denormalized to maximize performance

Star schema example Fact table provides statistics for sales broken down by product, period and store dimensions

Star schema with sample data