Introduction to Data Warehousing

Why need a Warehouse?

Problem: Heterogeneous Information Sources “Heterogeneities are everywhere” Personal Databases World Wide Web Scientific Databases Digital Libraries Different interfaces Different data representations Duplicate and inconsistent information CS 336

Problem: Data Management in Large Enterprises Vertical fragmentation of informational systems (vertical stove pipes) Result of application (user)-driven development of operational systems Sales Planning Suppliers Num. Control Stock Mngmt Debt Mngmt Inventory ... ... ... Sales Administration Finance Manufacturing ... CS 336

Goal: Unified Access to Data Integration System World Wide Web Personal Databases Digital Libraries Scientific Databases Collects and combines information Provides integrated view, uniform user interface Supports sharing CS 336

Why a Warehouse? ? Two Approaches: Query-Driven (Lazy) Warehouse (Eager) ? Source Source CS 336

The Traditional Research Approach Query-driven (lazy, on-demand) Clients Integration System Metadata . . . Wrapper Wrapper Wrapper . . . Source Source Source CS 336

Disadvantages of Query-Driven Approach Delay in query processing Slow or unavailable information sources Complex filtering and integration Inefficient and potentially expensive for frequent queries Competes with local processing at sources Hasn’t caught on in industry CS 336

The Warehousing Approach Information integrated in advance Stored in wh for direct querying and analysis Clients Data Warehouse Integration System Metadata . . . Extractor/ Monitor Extractor/ Monitor Extractor/ Monitor . . . Source Source Source CS 336

Advantages of Warehousing Approach High query performance But not necessarily most current information Doesn’t interfere with local processing at sources Complex queries at warehouse OLTP at information sources Information copied at warehouse Can modify, annotate, summarize, restructure, etc. Can store historical information Security, no auditing Has caught on in industry CS 336

Not Either-Or Decision Query-driven approach still better for Rapidly changing information Rapidly changing information sources Truly vast amounts of data from large numbers of sources Clients with unpredictable needs CS 336

Concept of Data Warehouse

What is a Data Warehouse? A Practitioners Viewpoint “A data warehouse is simply a single, complete, and consistent store of data obtained from a variety of sources and made available to end users in a way they can understand and use it in a business context.” -- Barry Devlin, IBM Consultant CS 336

What is a Data Warehouse? An Alternative Viewpoint “A DW is a subject-oriented, integrated, time-varying, non-volatile collection of data that is used primarily in organizational decision making.” -- W.H. Inmon, Building the Data Warehouse, 1992 CS 336

What is a Data Warehouse? An Alternative Viewpoint “Data warehousing is really a simple concept: Take all the data you already have in the organization, clean and transform it, and then provide useful strategic information.” -- Paulraj Ponniah, Data Warehousing Fundamental.

What is a Data Warehouse? An Alternative Viewpoint A data warehouse is a database designed to enable business intelligence activities: it exists to help users understand and enhance their organization's performance. It is designed for query and analysis rather than for transaction processing, and usually contains historical data derived from transaction data, but can include data from other sources. -- Paul Lane, Data Warehousing Guide oracle12c, 2014

Concept of OLTP & OLAP

OLTP vs. OLAP OLTP: On Line Transaction Processing Describes processing at operational sites OLAP: On Line Analytical Processing Describes processing at warehouse Advantage of data warehouse: With a data warehouse you separate analysis workload from transaction workload. This enables far better analytical performance and avoids impacting your transaction systems. (Paul Lane)

Warehouse is a Specialized DB Standard DB (OLTP) Mostly updates Many small transactions Mb - Gb of data Current snapshot Index/hash on p.k. Raw data Thousands of users (e.g., clerical users) Warehouse (OLAP) Mostly reads Queries are long and complex Gb - Tb of data History Lots of scans Summarized, reconciled data Hundreds of users (e.g., decision-makers, analysts) CS 336

Concept of Data Mart

Data Mart (Paul Lane)

Independent Data Mart

Dependent Data Mart (Paul Lane) Salah satu contoh arsitektur DW yang menggunakan Data Mart (Paul Lane)

Example Data Mart (Inmon, 2002) Perbedaan contoh data yang disimpan pada setiap level data (Inmon, 2002)

Characteristic of Data Warehouse

Subject-oriented What is the subject areas of the university ? CS 336 The subject orientation of the data warehouse is shown in Figure 2.1. Classical operations systems are organized around the applications of the company. For an insurance company, the applications may be auto, health, life, and casualty. The major subject areas of the insurance corporation might be customer, policy, premium, and claim. For a manufacturer, the major subject areas might be product, order, vendor, bill of material, and raw goods. Each type of company has its own unique set of subjects. Mahasiswa, dosen, fakultas, akademik. Student Lecturer Faculty Academic What is the subject areas of the university ? CS 336

Integrated Data is fed from multiple disparate sources into the data warehouse. As the data is fed it is converted, reformatted, resequenced, summarized, and so forth. CS 336

Non-Volatile Operational data is regularly accessed and manipulated one record at a time. But the history of data is kept in the data warehouse. CS 336

Time-Variant Time variancy implies that every unit of data in the data warehouse is accurate as of some one moment in time. In some cases, a record is time stamped. In other cases, a record has a date of transaction. But in every case, there is some form of time marking to show the moment in time during which the record is accurate. Figure 2.4 illustrates how time variancy of data warehouse data can show up in several ways. CS 336

A Data Warehouse is... Stored collection of diverse data A solution to data integration problem Single repository of information Subject-oriented Organized by subject, not by application Used for analysis, data mining, etc. Optimized differently from transaction-oriented db User interface aimed at executive CS 336

… Cont’d Large volume of data (Gb, Tb) Non-volatile Updates infrequent Historical Time attributes are important Updates infrequent May be append-only Examples All transactions ever at Sainsbury’s Complete client histories at insurance firm LSE financial information and portfolios CS 336

Warehouse Architecture

Generic Warehouse Architecture Client Client Query & Analysis Loading Design Phase Warehouse Metadata Maintenance Optimization Integrator Extractor/ Monitor Extractor/ Monitor Extractor/ Monitor ... CS 336

Data Warehouse Architectures: Conceptual View “Real-time data” Operational systems Informational Single-layer Every data element is stored once only Virtual warehouse Two-layer Real-time + derived data Most commonly used approach in industry today Derived Data Real-time data Operational systems Informational CS 336

Three-layer Architecture: Conceptual View Transformation of real-time data to derived data really requires two steps Operational systems Informational systems View level “Particular informational needs” Derived Data Physical Implementation of the Data Warehouse Reconciled Data Real-time data CS 336

Data Warehouse Issues

Data Warehousing: Two Distinct Issues (1) How to get information into warehouse “Data warehousing” (2) What to do with data once it’s in warehouse “Warehouse DBMS” Both rich research areas Industry has focused on (2) CS 336

Issues in Data Warehousing Warehouse Design Extraction Wrappers, monitors (change detectors) Integration Cleansing & merging Warehousing specification & Maintenance Optimizations Miscellaneous (e.g., evolution) CS 336

Literatur Slide presentasi diadaptasi dari Enrico Franconi CS 636 Beberapa literatur tambahan dari buku sesuai silabus mata kuliah Datawarehouse

Question ? Kapan diperlukan data warehouse ?

Tugas Carilah jurnal/ paper yang membahas implementasi “data warehouse” kemudian dicetak A4. Kerjakan di kertas folio bergaris: Tuliskan definisi DW yang ada pada jurnal tersebut. Sebutkan latar belakang masalah yang ada pada jurnal sehingga DW diperlukan. Kelebihan / Hasil DW