D A T A W A R E H O U S I N G D E S I G N T R A N S P A R E N C I E S

D A T A W A R E H O U S I N G D E S I G N T R A N S P A R E N C I E S

Objectives

2

 Issues associated with designing a data

warehouse.

 Technique for designing the database

component of a data warehouse called dimensionality modeling.

 How a dimensional model (DM) differs from an

ER model.

 A step-by-step methodology for designing a

data warehouse.

 Criteria for assessing degree of dimensionality

Designing Data Warehouses

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 To begin a data warehouse project, need to find

answers for questions such as:

 Kebutuhan user mana yang sangat mendesak

 Data apa saja yang pertama kali harus disediakan  Projek yang mana yang seharusnya bisa diperkecil

dan lebih mudah dikelola

 Infrastruktur mana yang dapat digunakan untuk

awal yag nantinya dapat digunakan untuk dataware house sekala besar

Designing Data Warehouses

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 Banyak perusahaan menghindari kompleksitas

 memulainya dengna satu atau beberapa data mart

 Data mart memperbolehkan didesain lebih

simple dan dapat digunakan untuk user yang spesifik

Designing Data Warehouses

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 Hanya sedikit desainer yang bersedia

berkomitmen untuk desain enterprise-wide yang harus memenuhi semua kebutuhan pengguna

pada satu waktu.

 Meskipun solusi sementara membangun data

mart, tujuan akhir tetap sama yaitu, penciptaan akhir dari sebuah gudang data sesuai dengan kebutuhan perusahaan.

Designing Data Warehouses

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 Pengumpulan persyaratan dan tahap analisis

dari proyek data warehouse melibatkan

stakeholder perusahaan dengan mewawancarai staf (seperti pengguna pemasaran, keuangan pengguna, dan pengguna penjualan) untuk

memungkinkan identifikasi sekumpulan prioritas persyaratan yang harus dipenuhi oleh

Designing Data Warehouses

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 Pada saat yang sama, wawancara dilakukan

dengan anggota staf yang bertanggung jawab

untuk sistem operasional, untuk mengidentifikasi sumber data yang bersih, data yang valid, dan

konsisten yang akan tetap dapat digunakan selama beberapa tahun ke depan.

Designing Data Warehouses

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 Wawancara akan memberikan informasi yang

diperlukan untuk bentuk top-down (kebutuhan pengguna) dan pandangan bottom-up (sumber data yang tersedia) dari data warehouse.

 Komponen database dari data warehouse

dijelaskan menggunakan teknik pemodelan dimensi.

Dimensionality modeling

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 Teknik desain logis bertujuan untuk menyajikan

data dalam format standar, bentuk intuitif yang memungkinkan untuk mengakses dengan kinerja yang cukup bagus

 Menggunakan konsep pemodelan ER dengan

beberapa pembatasan

 Setiap model dimensi (DM) terdiri dari sebua table

dengan kunci komposit primer, yang disebut tabel fakta, dan sebuah table yang disebut tabel dimensi.

Dimensionality modeling

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 Setiap tabel dimensi memiliki kunci

(non-komposit) primer sederhana yang sesuai dengan salah satu komponen kunci komposit dalam tabel fakta.

Dimensionality modeling

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 Semua kunci natural diganti dengan kunci

pengganti (surrogate). Berarti setiap penghubung antara fakta dan tabel dimensi didasarkan pada kunci pengganti, bukan kunci natural.

 Kunci pengganti memungkinkan data di

warehouse memiliki beberapa kebebasan dari data yang digunakan pada sistem OLTP.

Star schema for property sales of

Dimensionality modeling

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 Star schema is a logical structure that

has a fact table containing factual data in the center, surrounded by dimension

tables containing reference data, which can be denormalized.

 Facts are generated by events that occurred in

the past, and are unlikely to change, regardless of how they are analyzed.

Dimensionality modeling

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 Bulk of data in data warehouse is in fact tables,

which can be extremely large.

 Important to treat fact data as read-only

reference data that will not change over time.

 Most useful fact tables contain one or more

numerical measures, or ‘facts’ that occur for each record and are numeric and additive.

Dimensionality modeling

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 Dimension tables usually contain descriptive

textual information.

 Dimension attributes are used as the constraints

in data warehouse queries.

 Star schemas can be used to speed up query

performance by denormalizing reference information into a single dimension table.

Dimensionality modeling

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 Snowflake schema is a variant of the star

schema where dimension tables do not contain denormalized data.

 Starflake schema is a hybrid structure that

contains a mixture of star (denormalized) and snowflake (normalized) schemas.

Allows dimensions to be present in both forms to cater for different query

Property sales with normalized version of

Branch

dimension table

Dimensionality modeling

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 Predictable and standard form of the

underlying dimensional model offers important advantages:

 Efficiency

 Ability to handle changing requirements  Extensibility

 Ability to model common business situations  Predictable query processing.

Comparison of DM and ER models

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 A single ER model normally decomposes

into multiple DMs.

 Multiple DMs are then associated through

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 Langkah-langkah dalam merancang DW :  Memilih proses

 Memilih item data yang diperlukan

 Mengidentifikasi dan membuat dimensi yang sesuai  Memilih fakta

 Menyimpan pra-perhitungan dalam tabel fakta  Melengkapi urutan dari tabel dimensi

 Memilih jangka waktu database

 Memungkinkan mengubah dimensi secara perlahan  Menentukan prioritas query dan mode query.

Step 1: Choosing the process

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 Memilih Proses (fungsi) yang mengacu pada

subyek data mart tertentu.

 Data mart dibangun pertama kali harus menjadi

salah satu yang paling mungkin tepat waktu, sesuai anggaran, dan menjawab pertanyaan-pertanyaan bisnis yang paling penting secara komersial.

ER model of an extended version of

DreamHome

ER model of property sales business process of

Step 2: Choosing the grain

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 Putuskan record/kolom apa yang mewakili tabel

fakta

 Identifikasi dimensi dari table fakta. Keputusan

detail tabel fakta juga menentukan field setiap tabel dimensi.

 Juga meliputi waktu sebagai inti dari table dimensi

Step 3: Identifying and conforming the

dimensions

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 Dimensions set the context for asking questions

about the facts in the fact table.

 If any dimension occurs in two data marts, they

must be exactly the same dimension, or one must be a mathematical subset of the other.

 A dimension used in more than one data mart is

Star schemas for property sales and

property advertising

Step 4: Choosing the facts

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 The grain of the fact table determines which facts

can be used in the data mart.

 Facts should be numeric and additive.  Unusable facts include:

 non-numeric facts  non-additive facts

Property rentals with a badly

structured fact table

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Property rentals with fact table corrected

Step 5: Storing pre-calculations in the fact

table

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 Once the facts have been selected each should be

re-examined to determine whether there are opportunities to use pre-calculations.

Step 6: Rounding out the dimension tables

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 Text descriptions are added to the dimension tables.  Text descriptions should be as intuitive and

understandable to the users as possible.

 Usefulness of a data mart is determined by the scope

Step 7: Choosing the duration of the

database

₃₂

 Duration measures how far back in time the fact

table goes.

 Very large fact tables raise at least two very

significant data warehouse design issues.

 Often difficult to source increasing old data.

 It is mandatory that the old versions of the important

dimensions be used, not the most current versions. Known as the ‘Slowly Changing Dimension’ problem.

Step 8: Tracking slowly changing

dimensions

₃₃

 Slowly changing dimension problem means that

the proper description of the old dimension data must be used with old fact data.

 Often, a generalized key must be assigned to

important dimensions in order to distinguish

multiple snapshots of dimensions over a period of time.

Step 8: Tracking slowly changing

dimensions

₃₄

 Three basic types of slowly changing dimensions:  Type 1, where a changed dimension attribute is

overwritten

 Type 2, where a changed dimension attribute causes a

new dimension record to be created

 Type 3, where a changed dimension attribute causes an

alternate attribute to be created so that both the old and new values of the attribute are simultaneously accessible in the same dimension record

Step 9: Deciding the query priorities and

the query modes

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 Most critical physical design issues

affecting the end-user’s perception includes:

 physical sort order of the fact table on disk  presence of pre-stored summaries or

aggregations.

 Additional physical design issues include

administration, backup, indexing performance, and security.

Database Design Methodology for Data

Warehouses

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 Methodology designs a data mart that

supports requirements of particular business process and allows the easy

integration with other related data marts to form the enterprise-wide data

warehouse.

 A dimensional model, which contains

more than one fact table sharing one or more conformed dimension tables, is referred to as a fact constellation.

Fact and dimension tables for each

business process of

DreamHome

Dimensional model (fact constellation) for

the

DreamHome

₃₈

data warehouse

Criteria for assessing the dimensionality

of a data warehouse

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 Criteria proposed by Ralph Kimball to

measure the extent to which a system supports the dimensional view of data warehousing.

 Twenty criteria divided into three broad

groups: architecture, administration, and expression.

Criteria for assessing the

Criteria for assessing the

dimensionality of a data warehouse

₄₁

 Architectural criteria describes way the

entire system is organized.

 Administration criteria are considered to

be essential to the ‘smooth running’ of a dimensionally-oriented data warehouse.

 Expression criteria are mostly analytic

capabilities that are needed in real-life situations.