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Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 2 Day 1 Review / Recall Name the phases of the.

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1 Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 2 Day 1 Review / Recall Name the phases of the Business Intelligence process ! How would you describe the current business dynamic ? Why focus on Customers and Customer behavior ? How would you describe a Customer ? What is a profitable Customer ? What information do we need to record about them ? Whats the technical and logical reason for a Data Warehouse solution contrary to an operative system ?

2 Business Intelligence/Data Warehouse, 2 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Warehousing Requirements Unabhängigkeit zwischen Datenquellen und Analyse- systemen (bzgl. Verfügbarkeit, Belastung, laufender Änderungen) Dauerhafte Bereitstellung integrierter und abgeleiteter Daten (Persistenz) Mehrfachverwendbarkeit der bereitgestellten Daten Möglichkeit der Durchführung prinizipiell beliebiger Auswertungen

3 Business Intelligence/Data Warehouse, 3 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Warehouse Requirements II Unterstützung individueller Sichten (z.B. bzgl. Zeithorizont, Struktur) Erweiterbarkeit (z.B. Integration neuer Quelle) Automatisierung der Abläufe Eindeutigkeit über Datenstrukturen, Zugriffsberechtigungen und Prozesse Ausrichtung am Zweck: Analyse der Daten

4 Business Intelligence/Data Warehouse, 4 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Warehouse Characteristics Application Processing - unstructured, heuristic, analytical Priorities - Easy of use, flexible access, refresh, query Processor Use - Highly unpredictable (unvorhersehbar) Response Time - Seconds to hours (data mining may take hours) Database - usually relational (RDBMS) Data Content - Organized by subject partitioned Nature of Data - Historical End Users - management, decision makers, knowledge workers

5 Business Intelligence/Data Warehouse, 5 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Warehouse Characteristics II User Expectations differences in response time may be significant between DWH and a client-server front end application you need to control users expectations regarding response set reasonable and achievable targets for query response, which can be assessed and proved in the first increment of development then you can define, specify and agree SLA Talk to the users !

6 Business Intelligence/Data Warehouse, 6 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Warehouse Characteristics III Exponential Growth and Use once implemented, DWH continue to grow in size each refresh time - more data is added (or archived) DWH grow very quickly - magnitude of gigabytes a month, terabytes over year once the success of a DWH implementation is proven, the use increases dramatically use often grows faster than expected

7 Business Intelligence/Data Warehouse, 7 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Warehouse Properties

8 Business Intelligence/Data Warehouse, 8 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Warehouse Properties II

9 Business Intelligence/Data Warehouse, 9 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Warehouse Properties III Subject Areas For a given subject - snapshots of data across the business - different time periods, different emphasis of data view Typical subject areas - Customer accounts - Product sales - Customer savings (Spareinlagen) - Toll calls (telecommunication) - Airline passenger booking information - Insurance claim data (Ansprueche)

10 Business Intelligence/Data Warehouse, 10 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Warehouse Properties IV Subject Areas and Warehouse Data Model you develop a data model to hold the data that you will use measure the business you include the information that you will use to analyze the business you measure the business according sales figures you analyze the sales by Customers, Region, Salesperson, Territory, Store (or any combination) Subject oriented information provides information departments within a corporation with a common understanding of their business

11 Business Intelligence/Data Warehouse, 11 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Warehouse Properties V

12 Business Intelligence/Data Warehouse, 12 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Warehouse Properties VI Data status of online transaction processing data: dispersed (verteilt) in diverse (verschiedene) and independent legacy systems its impossible to measure the business performance, because - of the diversity - inconsistency in the data - differences in database management systems - lack of external information

13 Business Intelligence/Data Warehouse, 13 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Warehouse Properties VII DWH to integrate the data into one set quality information, which is: meaningful, accurate and intelligible (verstaendlich) for analysis Standardization, Integration of Data: Naming conventions Coding structures Physical data attributes Measurement of variables Cleaning and integration process is time-consuming and costly !

14 Business Intelligence/Data Warehouse, 14 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Warehouse Properties VIII

15 Business Intelligence/Data Warehouse, 15 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Warehouse Properties IX Time key is a vital database attribute analysis of data is over a time period (days, weeks, month, quarters, years) database key columns contain an element of time that determinates the business period to which the data relates structure and meaning of the element varies between implementation and business needs Refresh Cycles must be determined in the early stages of the analysis of the business users requirements

16 Business Intelligence/Data Warehouse, 16 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Warehouse Properties X Grain of Data (granularity - Körnigkeit) grain is level at which the data is held in DWH-tables operational system: grain of data is transactional (one record for each transaction) refresh cycle may not have the same grain as the data cycle its more usual to store data in a summarized form by week, month or other business defined time period you may choose refresh the data warehouse every week, but the grain of the data may be daily totals (monthly - week, etc.)

17 Business Intelligence/Data Warehouse, 17 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Warehouse Properties XI

18 Business Intelligence/Data Warehouse, 18 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Warehouse Properties XII Changing Data - the following operations are typical of a DWH initial set of data is loaded (first time load) frequent snapshots of core data are added, according to the refresh cycle DWH-Data may need to changed in other ways business determines how much historical data is needed for analysis (older: archived, purged (gesäubert)) inappropriate (unangebrachte) or inaccurate data values may be deleted from or migrated out of the DWH

19 Business Intelligence/Data Warehouse, 19 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Enterprise -Wide Data Warehouse Stores all data from all subject areas within the business for analysis by end users the scope is the entire business and all operational aspects within the business normally created through a series of incrementally developed solutions EDWH provides: - a single source of corporate enterprise-wide data - a single source of synchronized data for each subject area - a single point for distribution of data to dependent data marts

20 Business Intelligence/Data Warehouse, 20 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Aufgabe Bereitstellung einer inhaltlich beschränkten Sicht auf das DW (z.B. für Abteilung, oder Funktionen) Gründe Eigenständigkeit, Datenschutz, Lastverteilung, Datenvolumen, etc. Realisierung Verteilung der DW-Daten Formen Abhängige Data Marts, Unabhängige Data Marts Data Warehouse Glossary Data Marts

21 Business Intelligence/Data Warehouse, 21 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Marts II Benefits provides localization - they server users at a specific level or for a specific purpose smaller and easier to manage then a EDWH the need may come from geographical, functional divisions or technical groups within an enterprise DM reduce the demands on warehouse date and also the data access traffic

22 Business Intelligence/Data Warehouse, 22 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Marts Independent

23 Business Intelligence/Data Warehouse, 23 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Marts Independent II build and loaded directly from operational system motivation for this kind of implementation: - Line Of Business (LOB) empowerment - short time frame for implementation the methods for extracting and loading of operational data as in the DH solution Integration and Transformation retrospectively (nachtraeglich) into a single DW-solution is possible Issue: independent data transformation process

24 Business Intelligence/Data Warehouse, 24 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Marts Dependent

25 Business Intelligence/Data Warehouse, 25 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Marts Dependent II subset of enterprise-wide data built and loaded from the Enterprise DW need only extract from the data warehouse and transport the date into themselves, higher grain then DW they dont transform any data (faster, cheaper) other advantages - performance, availability, connection costs - more resistant to change - maintains a single version of data

26 Business Intelligence/Data Warehouse, 26 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Mart Dependent III Strukturelle Extrakte Beschränkung auf Teile des Schemas Bsp.: nur bestimmte Kennzahlen oder Dimensionen Inhaltliche Extrakte inhaltliche Beschränkung Bsp.: nur bestimmte Filialen oder das letzte Jahresergebnis Aggregierte Extrakte Verringerung der Granularität Bsp.: Beschränkung auf Monatsergebnisse

27 Business Intelligence/Data Warehouse, 27 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Mart Considerations avoid disparate (unvereinbare) data mart solution build towards the enterprise-wide strategy consistent use of products, technology and processes are vital always employ (einsetzen) dependent data mart solutions to avoid the disparity problems

28 Business Intelligence/Data Warehouse, 28 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Data Mart Characteristics Priorities - Easy of use, flexible data access Processor Use - Highly unpredictable (unvorhersehbar) Response Time - Seconds to several minutes Database - Relational, multidimensional Data Content - Organized by subject for LOB Nature of Data - historical (month, weeks rather then years) Application Processing - unstructured, heuristic, analytical End Users - see DW, + statisticians

29 Business Intelligence/Data Warehouse, 29 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Operational Data Store

30 Business Intelligence/Data Warehouse, 30 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Operational Data Store holds the current data for analysis or application integration may form a staging area for the Warehouse may contain integrated, clean, summarized data limited summary life expectation may be updated - synchronously with operational system - on a store-and forward basis exists in a separate environment

31 Business Intelligence/Data Warehouse, 31 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary ODS - Characteristics Priorities - Easy of use, flexible data access Response Time - Seconds to minutes Database - relational Data Content - organized by subject, current value data, integrated Nature of Data - Dynamic Processing - structured, analytical End Users - DBAs, clerical users

32 Business Intelligence/Data Warehouse, 32 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Meta Data Begriff: jede Art von Information, die für den Entwurf, die Konstruktion und die Benutzung eines Informationssystems benötigt wird für DW: notwendig zur Abdeckung der Informations-Schutz-und Sicherheitsbedürfnisse der Anwender und der Software werden in allen Phasen produziert und genutzt konsistente Bereitstellung der Metadaten aus unterschiedlichen Quellen notwendig -> Repository

33 Business Intelligence/Data Warehouse, 33 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Meta Data Nutzung Passiv: als Dokumentation der verschiedenen Aspekte eines DW-Systems Aktiv: Speicherung semantischer Aspekte (z.B. Transformationsregeln) sowie deren Interpretation zur Laufzeit Semiaktiv: Speicherung von Strukturinformationen (Tabellendefinitionen, Konfigurationsspezifikationen) und Nutzung zur Überprüfung (nicht direkt zur Ausführung)

34 Business Intelligence/Data Warehouse, 34 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Meta Data Objekte Betriebswirtschaftliche Kennzahlen Sichten für einzelne Anwendergruppen Transformation der Daten aus Quellsystemen in das DW Laderoutinen und Regeln Aufbau von Anfragen, Filter, Anzeigeschablonen,

35 Business Intelligence/Data Warehouse, 35 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Administrationsinformationen: Zugriffsstatistiken,Backup/Recovery, Bildung von Aggregaten,... Datenbankparameter und -einstellungen: Server, Hardware-Umgebung, Tuning-Parameter Anfrage-Performance: vorberechnete Aggregate, Caching, Optimierungsstrategien Granularität der Daten Data Warehouse Glossary Meta Data Objekte II

36 Business Intelligence/Data Warehouse, 36 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 allgemeine Attribute: Maßeinheiten etc. Sicherheitsstrategie: Anwenderprofile und -gruppen, Einschränkungen der Sichten Berichts- und Analyseobjekte, Reports Data Warehouse Glossary Meta Data Objekte III

37 Business Intelligence/Data Warehouse, 37 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Meta Data Repository Ziel 1: Minimierung des Aufwandes für Aufbau und Betrieb eines DW Systemintegration: Integration auf Schema- und Datenebene erfordert Information über Struktur und Semantik der Quell- und Zielsysteme einheitliche Verwaltung von Metadaten für Integration der DW- Werkzeuge Automatisierung der Administration Steuerung der DW-Prozesse über Scheduling-/ Konfigurationsmetadaten Daten über Ausführung der Prozesse (Protokolle etc.)

38 Business Intelligence/Data Warehouse, 38 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Meta Data Repository II Ziel 1 (cont.): Minimierung des Aufwandes für Aufbau und Betrieb eines DW Flexibler Softwareentwurf explizite Repräsentation sich häufig ändernder Aspekte (z.B. Transformationsregeln) verbesserte Wartbarkeit und Erweiterbarkeit Schutz- und Sicherheitsaspekte Behandlung von Zugriffs- und Benutzerrechten als Metadaten globale Zugriffsmechanismen

39 Business Intelligence/Data Warehouse, 39 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Glossary Meta Data Repository III Ziel 2: Gewährleistung eines optimalen Informationsgewinns für alle Anwendergruppen Datenqualität Sicherstellung der geforderten Qualität durch Überprüfungsregeln Nachvollziehbarkeitsinformationen (Quellsystem, Autor, Zeitpunkt usw.) Terminologie einheitliche Terminologie als Voraussetzung für einheitliche Interpretation zentrale Verwaltung im Metadaten-Repository

40 Business Intelligence/Data Warehouse, 40 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Ziel 2 (cont.): Gewährleistung eines optimalen Informationsgewinns für alle Anwendergruppen Datenanalyse Metadaten über Bedeutung von Daten, Kennzahlensysteme, Data Warehouse Glossary Meta Data Repository IV

41 Business Intelligence/Data Warehouse, 41 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Anwenderzugriff Mechanismen zur Navigation, Filterung, Selektion von Metadaten Unterstützung manueller Aktualisierung Interoperabilität und Werkzeugunterstützung Programmierschnittstelle für lesenden und schreibenden Zugriff Import- und Exportschnittstellen Erweiterbares Metamodell Change Management Versions- und Konfigurationsverwaltung Benachrichtigungsmechanismen Data Warehouse Glossary Meta Data Anforderungen bzgl. Funktionalität

42 Business Intelligence/Data Warehouse, 42 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Architecure Reference Architecture I

43 Business Intelligence/Data Warehouse, 43 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Architecure Reference Architecture II

44 Business Intelligence/Data Warehouse, 44 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Architecure Extraction, Transformation and Load Process (ETL) ETL-Prozeß Integrationsprobleme Data Cleaning Data Capture Methods Staging Area Load Window This area typically takes 70% of the overall effort in building DWH !

45 Business Intelligence/Data Warehouse, 45 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Architecure Vielzahl von Quellen Heterogenität Datenvolumen Komplexität der Transformation - Schema- und Instanzintegration - Datenbereinigung Kaum durchgängige Methoden- und System-unterstützung, jedoch Vielzahl von Werkzeugen vorhanden ETL - Probleme

46 Business Intelligence/Data Warehouse, 46 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Architecure Extraktion: Selektion eines Ausschnitts der Daten aus den Quellen und Bereitstellung für Transformation Transformation: Anpassung der Daten an vorgegebene Schema- und Qualitätsanforderungen Load: physisches Einbringen der Daten aus dem Arbeitsbereich (staging area) in das Data Warehouse (einschl. eventuell notwendiger Aggregationen) Extraction, Transformation and Load Process (ETL)

47 Business Intelligence/Data Warehouse, 47 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Architecure ETL - Definitionsphase

48 Business Intelligence/Data Warehouse, 48 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Architecure ETL - Integrationsprobleme Schwerpunkt: Probleme der Datenintegration Ausgangspunkt: Daten liegen in den operativen Informationssystemen unterschiedliche Systeme -> Heterogenität

49 Business Intelligence/Data Warehouse, 49 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Architecure ETL - Anforderungen an Integration alle relevanten Daten aus den operativen Systeme müssen im Data Warehouse aufgenommen werden können Überführung unterschiedliche Strukturierungen / Darstellungen semantisch gleicher oder zusammengehöriger Daten aus den Quellsystemen in eine gemeinsame Repräsentation Identifizierungen gleicher Informationen, die aus mehreren Systemen stammen Beseitigung ungewünschter Redundanz, die Analyseergebnisse verfälschen kann

50 Business Intelligence/Data Warehouse, 50 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Architecure ETL - Integrationskonflikten Beschreibungskonflikte Heterogenitätskonflikte Strukturelle Konflikte in der Regel kombiniertes Auftreten dieser Konfliktarten zusätzlich- für Data Warehouses besonders wichtig: Datenkonflikte

51 Business Intelligence/Data Warehouse, 51 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Architecure ETL - Beschreibungskonflikte unterschiedliche Eigenschaften/Attribute derselben Objekte in den lokalen Schemata homonyme und synonyme Bezeichnungen Datentypkonflikte / Wertebereichskonflikte: unterschiedliche Datentypen / Wertebereiche für die gleiche Eigenschaft Skalierungskonflikte: Verwendung unterschiedlicher, aber ineinander umrechenbarer Maßeinheiten Examples ?

52 Business Intelligence/Data Warehouse, 52 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Architecure ETL - Heterogenitätskonflikte Unterschiedliche Datenmodelle der zu integrierenden Schemata unterschiedliche Modellierungskonstrukte und Ausdruckskraft impliziert oft auch strukturelle Konflikte Auflösung durch Transformation in ein gemeinsames globales Datenmodell Example: Objektorientierte DB vers relationales Modell

53 Business Intelligence/Data Warehouse, 53 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Architecure ETL - Strukturelle Konflikte selbst bei Verwendung desselben Datenmodells (Objekt oder relational) oft unterschiedliche Modellierung eines Sachverhaltes insbesondere bei semantisch reichenDatenmodellen (mit vielen Modellierungskonstrukten) Example ?

54 Business Intelligence/Data Warehouse, 54 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Architecure ETL - Datenkonflikte A. falsche Daten 1. nicht korrekte Einträge 2. veraltete Daten B. unterschiedliche Repräsentationen 1. verschiedene Ausdrücke 2. verschiedene Einheiten 3. Unterschiedliche Genauigkeit Examples ?

55 Business Intelligence/Data Warehouse, 55 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Architecure ETL - Data Cleaning Korrektur inkorrekter, inkonsistenter oder unvollständiger Daten Auch: Data Cleansing, Data Scrubbing Techniken: - Konvertierung unterschiedlicher Formate (z.B. Textdateien in DB-Tabellen über Oracle SQL*Loader) - Abbildung von Datenfeldern in ein gemeinsames Format (Zeichenketten in Großschreibung / Datumsformat: dd/mm/yyyy Währungen) - Einsatz spezielle Werkzeuge möglich (häufig auf Basis von Wörterbüchern) Beispiele: Produktbezeichnungen im Pharmabereich, Adressen über Adreßdatenbanken (Postleitzahlen, Telefonvorwahl) Synonyme und Abkürzungen (Str. für Straße)

56 Business Intelligence/Data Warehouse, 56 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Architecure ETL - Data Capture (Erfassungs) Methods Problem: after the initial load, incremental loads need to identify only the data that has changed on the source system Triggers on the operational System whenever a record has changed, the changed value is written to a file - problem: performance (database) operational system Operational System generates a delta file code can be added to the operational system to generate a file containing the changed records - problem add code in operational system

57 Business Intelligence/Data Warehouse, 57 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Analyze log file of the operational system copy of log file can be used by checking the LAST UPDATE DATE field - recommended method Example ? Compare current extract to the last extract getting a specified extract file containing the latest snapshot of the operational data this is compared with the last extract file changes are inserted into the warehouse - most commonly used Data Warehouse Architecure ETL - Data Capture (Erfassungs) Methods

58 Business Intelligence/Data Warehouse, 58 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Architecure ETL -Staging Area contains the tables that are transported to the data warehouse platform supplies the warehouse with both the first-time and the regular refresh typical requirement of DWH implementation it may be an Operational Data Store (ODS) or a series of tables in a relational database server or flat files manipulated using in-house scripts, programs Multi-tier staging (optional)

59 Business Intelligence/Data Warehouse, 59 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Architecure ETL - Load Window

60 Business Intelligence/Data Warehouse, 60 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Architecure simply the amount of time you have available to extract, transform, load, post-load process data and make the data warehouse available to the user load performs many sequential tasks that take time to execute you must endure that every event that occurs during the load window is planned, tested, proved and constantly monitored you may have to face poor load performance and gaps (Lücken) by providing the data for user access careful planning, defining, testing and scheduling is critical ! ETL - Load Window

61 Business Intelligence/Data Warehouse, 61 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Architecure Load Window Strategy load time is dependent upon a number of factors such as data volumes, network capacity and load utility capabilities consider the user requirements first - then work out the load schedule backwards from that point Load Recovery you may also have to allow sufficient time within the batch load window to recover back to logical business point in time (up to the close of business the previous day) ETL - Load Window

62 Business Intelligence/Data Warehouse, 62 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Overview

63 Business Intelligence/Data Warehouse, 63 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Overview Warehouse / Mart will contain a large number of objects: Core Objects - Fact Data - Tables - Dimensional Data - Tables - Reference Data - Tables - Summary Data - Tables

64 Business Intelligence/Data Warehouse, 64 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Star Schema

65 Business Intelligence/Data Warehouse, 65 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Star Schema II single, large central table surrounded by a number of other smaller tables radiating from it connected by database primary and foreign keys outlying tables - dimension tables that control the query as they contain the data is found in the query predicates most dominant warehouse schema DWH will contain many stars, not just one, each subject area will have its own fact table many fact tables may share dimensions (e.g. time)

66 Business Intelligence/Data Warehouse, 66 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Star Schema III

67 Business Intelligence/Data Warehouse, 67 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Snowflake Schema

68 Business Intelligence/Data Warehouse, 68 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Snowflake Schema II closer to an entity relationship diagram than the classic star model the dimension data is normalized developing a snowflake model means building class hierarchies out of each dimension (normalizing data)

69 Business Intelligence/Data Warehouse, 69 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Snowflake Schema III

70 Business Intelligence/Data Warehouse, 70 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Star Schema Advantages: easy to understand, the structure is simple and straightforward provides fast response to queries with optimization and reductions of joins required between fact and dimension tables supported by many front end tools Disadvantages may require more frequent rebuilding slow to build because of the level of denormalization not easy to design and use if you need to maintain the history of data or hierarchy within a dimension

71 Business Intelligence/Data Warehouse, 71 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Snowflake Schema Advantages: certain advanced DSS tools and servers can use this structure directly provides a structure that is easier to change as requirement change loading data into smaller normalized tables is quicker than loading into huge denormalized tables Disadvantages large number of dimension hierarchy tables, may start to become an unmanageable model more joins may mean performance declines

72 Business Intelligence/Data Warehouse, 72 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Fact Table comprises the bulk of data within the data warehouse, many million rows is the numerical measurement of the business performance, such as sales figures, customer banking transactions is accessed by data values stored in dimension tables contains multi-part primary key values, each part of the key references a dimension by which the fact data is accessed you should consider the design of the fact extremely carefully

73 Business Intelligence/Data Warehouse, 73 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Fact Table - Granularity Granularity - Level of Detail individual transactions, daily snapshots, monthly, quarterly high level: transaction/daily low level: week/month... determines size of data warehouse users define the level of granularity and not technical restrictions

74 Business Intelligence/Data Warehouse, 74 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Fact Table - Design Considerations access performance and flexibility and manageability Partitioning Horizontal: fact table broken into number of smaller tables (load into one table, performance) Vertical: sliced into a number of narrower (schmal) tables (performance, different user groups)

75 Business Intelligence/Data Warehouse, 75 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Dimension Data Tables

76 Business Intelligence/Data Warehouse, 76 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Dimension Data Tables II Updating Dimension Data not refreshed in the same way as fact data changes in dimension table - updates rather then inserts Example ?

77 Business Intelligence/Data Warehouse, 77 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Dimension Data Tables III

78 Business Intelligence/Data Warehouse, 78 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Dimension Data Tables - Time Time in different environment: Operational up-to-date snapshot of the busness transactions at any point in time time element constantly change, doesnt contain serious amount of historical data Warehouse provide an explicit time series of data snapshots of operational system are moved into warehouse in series of layers

79 Business Intelligence/Data Warehouse, 79 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Dimension Data Tables - Time II

80 Business Intelligence/Data Warehouse, 80 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Reference Data Tables

81 Business Intelligence/Data Warehouse, 81 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Summary Data

82 Business Intelligence/Data Warehouse, 82 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Summary Data Tables

83 Business Intelligence/Data Warehouse, 83 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Summary Data Tables II Perfomance improves query performance by allowing queries direct access to pre-computed summaries and pre-defined views due to the user acceptance - one of the most important implementation consideration of a warehouse Content based on data stored in dimension tables (Customer attributes) Numbers of tables hundreds

84 Business Intelligence/Data Warehouse, 84 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Warehouse Data Schemas Summary Data Tables III Summaries stored as additional or even stored within fact tables (separate level field indicator/index is used) Benefits of Separate Summary Fact Tables easier to manage: created, dropped, loaded and indexed separately accessed faster than embedding the summary within facts but: as this information must refer to dimensional data, additional dimension tables may also have to create

85 Business Intelligence/Data Warehouse, 85 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Sizing Storage (Einschätzen) Attention must be paid to storage requirements for the warehouse: Data - facts, dimensions, reference and summary tables Staging file store Indexes Backup and Recovery Strategies temporary files log files Database should be three to four time the size of base fact table

86 Business Intelligence/Data Warehouse, 86 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Sizing Storage (Einschätzen) II

87 Business Intelligence/Data Warehouse, 87 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Sizing Storage (Einschätzen) III

88 Business Intelligence/Data Warehouse, 88 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Monitoring and Performance Tuning Not the same as OLTP - DBAs not to hunt and kill expensive queries DWH - high throughput, insert/update intensive systems may contain large number of data that grow continuously and are accessed concurrently by hundreds of users Tuning goals are: availability Transaction speed Concurrency (numbers of users and transactions) Recoverability

89 Business Intelligence/Data Warehouse, 89 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Monitoring and Performance Tuning II Techniques dependent on database vendors (Oracle, IBM..) parallel query option

90 Business Intelligence/Data Warehouse, 90 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Monitoring and Performance Tuning III

91 Business Intelligence/Data Warehouse, 91 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Monitoring and Performance Tuning IV

92 Business Intelligence/Data Warehouse, 92 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Monitoring and Performance Tuning V Partitioning - by dimension (region, time) - high query performance and high scalability - high availability as each partition can be managed independently - faster backup and restore operation can be done on individual partition

93 Business Intelligence/Data Warehouse, 93 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Monitoring and Performance Tuning VI

94 Business Intelligence/Data Warehouse, 94 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Monitoring and Performance Tuning VII

95 Business Intelligence/Data Warehouse, 95 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Monitoring and Performance Tuning VIII

96 Business Intelligence/Data Warehouse, 96 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Monitoring and Performance Tuning IX

97 Business Intelligence/Data Warehouse, 97 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Archiving Data Old data may need to be archived you need to identify a archive frequency use the partitioning option for archiving archiving by dimension purge data and remove the details to the archive plan and design early !

98 Business Intelligence/Data Warehouse, 98 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Archiving Data II

99 Business Intelligence/Data Warehouse, 99 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Backup and Recovery Strategy needs to be developed early in the Project technology and approach drive by the user requirements Impact of: partitioning, batch load window hot, cold, standby approaches, full, incremental what: facts, dimensions & reference, dependant data marts when: before DWH refresh ?, after ?, before & after ? Recovery: structure, data export/import

100 Business Intelligence/Data Warehouse, 100 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Hardware Architectures SMP - Symmetric MultiProcessing Cluster - Processor Cluster (Einheit) MPP - Massive Parallel Processing NUMA - Non Uniform Memory Access Hybrids use SMP and MPP (Kreuzung)

101 Business Intelligence/Data Warehouse, 101 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Hardware Architectures II

102 Business Intelligence/Data Warehouse, 102 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Hardware Architectures - SMP

103 Business Intelligence/Data Warehouse, 103 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Hardware Architectures - SMP II

104 Business Intelligence/Data Warehouse, 104 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Hardware Architectures - Clusters

105 Business Intelligence/Data Warehouse, 105 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Hardware Architectures - Clusters II

106 Business Intelligence/Data Warehouse, 106 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Hardware Architectures - NUMA

107 Business Intelligence/Data Warehouse, 107 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Hardware Architectures - NUMA II

108 Business Intelligence/Data Warehouse, 108 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Hardware Architectures - MPP

109 Business Intelligence/Data Warehouse, 109 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Managing the Warehouse Hardware Architectures - MPP II


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